This document proposes a framework for identifying magnet communities in social networks. It discusses challenges in identifying magnet communities using existing methods like PageRank. The proposed framework models attractiveness of communities using standalone and dependency features. Standalone features consider attributes of individual communities, while dependency features capture attention flow between communities. An optimization problem is formulated to compute magnetism values for communities subject to constraints ensuring results are consistent with properties of magnet communities. Experimental results on employee and company networks demonstrate the framework outperforms baselines in ranking communities according to ground truths.
The Strength of Indirect Relations in Social Networksmosabou
Here, our goal is to develop a formal analysis of dual network structures taking into account transitive completions of higher order than the usually considered triadic closure, i.e., allowing quadruple closure and any other higher order transitive completion of open n-paths traversing two dual social networks. In this way, a sequence of indirect relations might be gen- erated in each social network, right next to the inherent direct relations in these networks. For this purpose, we introduce the setting of a “dual social network system” and we discuss how this setting might be produced in empirical situations, in which social networks are composed or partitioned in terms of various forms of actors’ categorizations from an attributional, attitudinal, typological or structural point of view. Furthermore, we are concerned with the issue of adjusting the concepts of Granovetter’s thesis on the strength of the weak ties to the case of direct and (any order) indirect relations in such dual social network systems.
Magnet community identification on social networksmoresmile
This document proposes a topic modeling approach called cross-collection topic-aspect model (ccTAM) to generate complementary summaries from news and social media streams. ccTAM jointly discovers representative and complementary information from the two sources by combining a two-dimensional topic-aspect model with a cross-collection method. It also introduces a measure to assess sentence-level complementarity and generates summaries by co-ranking news sentences and tweets based on their complementary relationship.
Short version of Dominating Sets, Multiple Egocentric Networks and Modularity...Moses Boudourides
Short version of Dominating Sets, Multiple Egocentric Networks and Modularity Maximizing Clustering of Social Networks. By M.A. Boudourides & S.T. Lenis
This document discusses facial recognition techniques using principal component analysis (PCA). It explains that PCA is used to reduce a large set of face image variables to a smaller set of principal components, or "eigenfaces", that contain most of the information. The document outlines how PCA is applied to a training set of face images to calculate the eigenfaces, which form an orthonormal basis set that can be used to reconstruct face images. It notes some challenges like variations in lighting and expressions but overall finds eigenface-based facial recognition to be a robust technique for security applications.
Topics of Complex Social Networks: Domination, Influence and AssortativityMoses Boudourides
The document summarizes topics related to complex social networks, including domination, influence, and assortativity. It begins by defining dominating sets in graphs and their properties such as minimal dominating sets. It describes the complexity of computing dominating sets and provides algorithms. It then discusses egocentric subgraphs induced by dominating sets and the classification of vertices as private or public alters. Finally, it introduces notation used to describe edges between dominating sets, private alters, and public alters.
A Study of the effects of emotions and Personality on Physical Health using I...ijdmtaiir
Emotions have a significant influence on the human
performance and intelligent behavior.As a negative emotion,
anger is the main cause in destroying one’s happiness. Also the
effects of anger are stress, fear etc., and they play a major role
in building a negative personality. The personality plays a
vital role in affecting states of emotions in any specific
situations. In this paper, we analyzethe emotion‘anger’which
affects physical health by relating with the dimensions of
personality using Induced Neutrosophic Relational Maps.
Section one describes problem of study. Section two gives the
information on the development of Induced Linked
Neutrosophic Relational Maps. Section three, the adaption of
the problem using Induced Linked Neutrosophic Relational
Maps (ILNRMs). Section four,conclusion and scope for
futurestudy.
Boudourides & Lenis, Distribution of Groups of Vertices Across Multilayer Net...Moses Boudourides
This document defines concepts related to the distribution of groups of vertices across multilayer networks. It defines a multilayer network as an edge-colored graph with m layers represented by m colors, where edges within each layer have one color and inter-layer edges have an (m+1)th color. It defines subgraphs and partitions of vertices into groups as either homogeneous if all vertices are in the same layer, or mixed if vertices are in multiple layers. The document applies these concepts to analyze a 3-layer Twitter network of retweets, follower relationships, and co-occurring hashtags.
The document summarizes key concepts from Chapter 8 of the textbook "Fundamentals of Multimedia" on lossy compression algorithms. It introduces lossy compression and discusses distortion measures, rate-distortion theory, quantization techniques including uniform, non-uniform, and vector quantization. It also covers transform coding techniques such as the discrete cosine transform and its use in image compression standards to remove spatial redundancies by transforming pixel values into frequency coefficients.
The Strength of Indirect Relations in Social Networksmosabou
Here, our goal is to develop a formal analysis of dual network structures taking into account transitive completions of higher order than the usually considered triadic closure, i.e., allowing quadruple closure and any other higher order transitive completion of open n-paths traversing two dual social networks. In this way, a sequence of indirect relations might be gen- erated in each social network, right next to the inherent direct relations in these networks. For this purpose, we introduce the setting of a “dual social network system” and we discuss how this setting might be produced in empirical situations, in which social networks are composed or partitioned in terms of various forms of actors’ categorizations from an attributional, attitudinal, typological or structural point of view. Furthermore, we are concerned with the issue of adjusting the concepts of Granovetter’s thesis on the strength of the weak ties to the case of direct and (any order) indirect relations in such dual social network systems.
Magnet community identification on social networksmoresmile
This document proposes a topic modeling approach called cross-collection topic-aspect model (ccTAM) to generate complementary summaries from news and social media streams. ccTAM jointly discovers representative and complementary information from the two sources by combining a two-dimensional topic-aspect model with a cross-collection method. It also introduces a measure to assess sentence-level complementarity and generates summaries by co-ranking news sentences and tweets based on their complementary relationship.
Short version of Dominating Sets, Multiple Egocentric Networks and Modularity...Moses Boudourides
Short version of Dominating Sets, Multiple Egocentric Networks and Modularity Maximizing Clustering of Social Networks. By M.A. Boudourides & S.T. Lenis
This document discusses facial recognition techniques using principal component analysis (PCA). It explains that PCA is used to reduce a large set of face image variables to a smaller set of principal components, or "eigenfaces", that contain most of the information. The document outlines how PCA is applied to a training set of face images to calculate the eigenfaces, which form an orthonormal basis set that can be used to reconstruct face images. It notes some challenges like variations in lighting and expressions but overall finds eigenface-based facial recognition to be a robust technique for security applications.
Topics of Complex Social Networks: Domination, Influence and AssortativityMoses Boudourides
The document summarizes topics related to complex social networks, including domination, influence, and assortativity. It begins by defining dominating sets in graphs and their properties such as minimal dominating sets. It describes the complexity of computing dominating sets and provides algorithms. It then discusses egocentric subgraphs induced by dominating sets and the classification of vertices as private or public alters. Finally, it introduces notation used to describe edges between dominating sets, private alters, and public alters.
A Study of the effects of emotions and Personality on Physical Health using I...ijdmtaiir
Emotions have a significant influence on the human
performance and intelligent behavior.As a negative emotion,
anger is the main cause in destroying one’s happiness. Also the
effects of anger are stress, fear etc., and they play a major role
in building a negative personality. The personality plays a
vital role in affecting states of emotions in any specific
situations. In this paper, we analyzethe emotion‘anger’which
affects physical health by relating with the dimensions of
personality using Induced Neutrosophic Relational Maps.
Section one describes problem of study. Section two gives the
information on the development of Induced Linked
Neutrosophic Relational Maps. Section three, the adaption of
the problem using Induced Linked Neutrosophic Relational
Maps (ILNRMs). Section four,conclusion and scope for
futurestudy.
Boudourides & Lenis, Distribution of Groups of Vertices Across Multilayer Net...Moses Boudourides
This document defines concepts related to the distribution of groups of vertices across multilayer networks. It defines a multilayer network as an edge-colored graph with m layers represented by m colors, where edges within each layer have one color and inter-layer edges have an (m+1)th color. It defines subgraphs and partitions of vertices into groups as either homogeneous if all vertices are in the same layer, or mixed if vertices are in multiple layers. The document applies these concepts to analyze a 3-layer Twitter network of retweets, follower relationships, and co-occurring hashtags.
The document summarizes key concepts from Chapter 8 of the textbook "Fundamentals of Multimedia" on lossy compression algorithms. It introduces lossy compression and discusses distortion measures, rate-distortion theory, quantization techniques including uniform, non-uniform, and vector quantization. It also covers transform coding techniques such as the discrete cosine transform and its use in image compression standards to remove spatial redundancies by transforming pixel values into frequency coefficients.
This document provides an overview of deep learning tutorials and the deep learning landscape. It discusses the evolution of machine learning from 2012 to the present, focusing on developments in deep neural networks. It outlines popular deep learning system architectures including distributed architectures, standalone toolkits, and bleeding edge directions like convolutional neural networks, LSTMs, memory networks, reinforcement learning, and generative models. The document aims to give readers an introduction to the key concepts and industry applications of deep learning.
This document provides an introduction to deep learning for natural language processing (NLP) over 50 minutes. It begins with a brief introduction to NLP and deep learning, then discusses traditional NLP techniques like one-hot encoding and clustering-based representations. Next, it covers how deep learning addresses limitations of traditional methods through representation learning, learning from unlabeled data, and modeling language recursively. Several examples of neural networks for NLP tasks are presented like image captioning, sentiment analysis, and character-based language models. The document concludes with discussing word embeddings, document representations, and the future of deep learning for NLP.
This document provides an introduction to deep learning. It discusses the history of machine learning and how neural networks work. Specifically, it describes different types of neural networks like deep belief networks, convolutional neural networks, and recurrent neural networks. It also covers applications of deep learning, as well as popular platforms, frameworks and libraries used for deep learning development. Finally, it demonstrates an example of using the Nvidia DIGITS tool to train a convolutional neural network for image classification of car park images.
What Deep Learning Means for Artificial IntelligenceJonathan Mugan
This document provides an overview of deep learning and its applications. It discusses how deep learning uses neural networks with many layers to learn representations of data, such as images and text, in an automated way. For computer vision, deep learning has made major improvements in tasks like object recognition, surpassing human-level performance. Deep learning has also been applied successfully to natural language processing tasks like learning word embeddings. The document suggests deep learning is an important development for achieving more broadly intelligent artificial systems.
This document discusses Google's use of deep learning and their DistBelief framework. It summarizes that Google is using deep learning to tackle large-scale problems with large models and datasets. The DistBelief framework allows deep learning models to be partitioned across multiple machines and cores for distributed training, using both model parallelism and data parallelism. This enables training on billions of examples using over 100,000 cores. Applications discussed include voice search, photo search, and text understanding.
Deep Learning with Python: Getting started and getting from ideas to insights in minutes.
PyData Seattle 2015
Alex Korbonits (@korbonits)
This presentation was given July 25, 2015 at the PyData Seattle conference hosted by PyData and NumFocus.
Effizientes Crawling für Websites. Anleitung um GoogleBot die Arbeit leichter zu machen. Besseres Ranking durch Crawl Budget Optimierung. Wichtige Hinweise zu Onsite SEO, Panda Diät, Panda Update, etc.
SEO-Vortrag SMX München 2016
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
The document discusses keyword research and strategies for understanding user intent. It provides tips for mapping keywords to user journeys and personas to gain deeper insights. Various tools for keyword research are also mentioned, including APIs that can be used to gather additional data without coding. Pivot charts and other visualizations are suggested to analyze keyword opportunities based on metrics like search volume and difficulty.
The document discusses Google's evolving approach to providing answers to user questions directly in search results through features like Knowledge Panels, Knowledge Graphs, and Featured Snippets. It notes how site content can be included in these answer features if it is highly relevant and ranked, and provides tips on keyword research and optimization to better target question-based queries and have content selected for the answer features.
Search engine optimization (SEO) is the process of affecting the visibility of a website or a web page in a search engine's "natural" or un-paid ("organic") search
Read all SEO Tips Shared by Matt Cutts in this PPT.
This document provides a summary of Michael King's presentation on the technical SEO renaissance. It discusses how SEO has evolved over time from basic tricks to a more technical focus as search engines have advanced. Key points include the growing importance of JavaScript, single page applications, HTTP headers, log file analysis, headless browsing, scraping techniques, content optimization using entities, internal linking structures, page speed optimizations, and preloading directives. The presentation argues that technical skills are now essential for SEOs to understand new developments and effectively optimize websites.
Fight Back Against Back: How Search Engines & Social Networks' AI Impacts Mar...Rand Fishkin
Rand's presentation on machine learning and deep learning in Google, Facebook, and beyond, and how engagement reputation will become key to every online marketing effort.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
Study on Impact of Media on Education Using Fuzzy Relational MapsMangaiK4
Abstract- In this paper we bring out the depth of impact of media upon the growth of education. Education moulds an individual to take firm decision on issues. It makes to feel independent and leads to a more exposed world. Media is a very powerful tool to explore the world and have access to the world. Internet, Mobile phones etc, helps for an easy access to any part of the world at out finger tips. Media may lead us to both constructive and destructive mechanism depending the way we deal with it. Here we use FRM model to study and analyze the impact of media on education.
This document proposes using spectral clustering based on the normalized graph Laplacian spectrum to solve problems in community detection and handwritten digit recognition. It summarizes the key concepts in graph signal processing and introduces spectral clustering. The paper provides a mathematical proof that the signs of the second eigenvector components of the normalized graph Laplacian can accurately partition a graph into two communities. It then applies this spectral clustering method to community detection and digit recognition, comparing results to other popular algorithms to demonstrate the advantages of the spectral clustering approach.
The document presents an algorithm for cooperative particle filtering for sensor network localization. It describes a distributed cooperative particle filter (CoopPF) that allows nodes to estimate their unknown locations by exploiting inter-node ranging measurements and communicating location probability distributions. The algorithm factorizes weight calculations to allow an iterative distributed implementation. It also proposes parametric distribution approximations to further reduce communication costs. Simulation results show the CoopPF and variants achieve accurate localization and perform better than existing methods in terms of mean square error over time and ranging noise levels.
This document provides an overview of mobile data offloading techniques for next generation cellular networks. It discusses the expected growth in mobile data traffic and need for offloading to WiFi networks. It presents a model for the offloading system involving mobile network operators, base stations and access points. It formulates the offloading problem as an optimization to maximize social welfare. An iterated double auction mechanism is proposed to solve the optimization in a distributed manner while achieving the desired economic properties. Results show the mechanism enables the requests and admissions to converge over iterations, minimizing the demand gap.
This document proposes methods for discovering organizational structure in static and dynamic social networks. For static networks, it introduces an m-Score to represent member importance and builds a community tree to represent the organizational hierarchy. For dynamic networks, it develops a tree learning algorithm to reconstruct the evolving community tree based on scoring past and current community structures. Experiments on karate club and Enron email networks demonstrate the approach.
A Study of the attitudes of road user in Enhancing the Gross National Happine...ijdmtaiir
This document summarizes a study that uses Combined Fuzzy Cognitive Maps (CFCMs) to analyze the attitudes of road users and how they impact gross national happiness. Ten attributes related to road use were identified by experts, including increased vehicle numbers, use of communication devices while driving, and disobeying traffic rules. Three experts each provided a connection matrix representing the causal relationships between attributes. The combined connection matrix was analyzed using initial state vectors, and a fixed point was reached where all attributes were in the "on" state, indicating increased vehicle numbers causes all other attributes to be active. The study concludes that obeying traffic rules and managing population growth can help create an accident-free society and enhance well-being and happiness.
The document discusses basic concepts related to continuous functions. It begins with an introduction and motivation for studying continuous functions. Some key reasons mentioned are that continuous functions are needed for integration and as underlying functions in differential equations. The document then provides definitions of limits and continuity in terms of limits. It gives examples of determining limits and continuity for various functions. Contributors to the field like Bolzano, Cauchy, and Weierstrass are also acknowledged. The document concludes with additional definitions of continuity, examples, and discussions of uniform continuity.
This document provides an overview of deep learning tutorials and the deep learning landscape. It discusses the evolution of machine learning from 2012 to the present, focusing on developments in deep neural networks. It outlines popular deep learning system architectures including distributed architectures, standalone toolkits, and bleeding edge directions like convolutional neural networks, LSTMs, memory networks, reinforcement learning, and generative models. The document aims to give readers an introduction to the key concepts and industry applications of deep learning.
This document provides an introduction to deep learning for natural language processing (NLP) over 50 minutes. It begins with a brief introduction to NLP and deep learning, then discusses traditional NLP techniques like one-hot encoding and clustering-based representations. Next, it covers how deep learning addresses limitations of traditional methods through representation learning, learning from unlabeled data, and modeling language recursively. Several examples of neural networks for NLP tasks are presented like image captioning, sentiment analysis, and character-based language models. The document concludes with discussing word embeddings, document representations, and the future of deep learning for NLP.
This document provides an introduction to deep learning. It discusses the history of machine learning and how neural networks work. Specifically, it describes different types of neural networks like deep belief networks, convolutional neural networks, and recurrent neural networks. It also covers applications of deep learning, as well as popular platforms, frameworks and libraries used for deep learning development. Finally, it demonstrates an example of using the Nvidia DIGITS tool to train a convolutional neural network for image classification of car park images.
What Deep Learning Means for Artificial IntelligenceJonathan Mugan
This document provides an overview of deep learning and its applications. It discusses how deep learning uses neural networks with many layers to learn representations of data, such as images and text, in an automated way. For computer vision, deep learning has made major improvements in tasks like object recognition, surpassing human-level performance. Deep learning has also been applied successfully to natural language processing tasks like learning word embeddings. The document suggests deep learning is an important development for achieving more broadly intelligent artificial systems.
This document discusses Google's use of deep learning and their DistBelief framework. It summarizes that Google is using deep learning to tackle large-scale problems with large models and datasets. The DistBelief framework allows deep learning models to be partitioned across multiple machines and cores for distributed training, using both model parallelism and data parallelism. This enables training on billions of examples using over 100,000 cores. Applications discussed include voice search, photo search, and text understanding.
Deep Learning with Python: Getting started and getting from ideas to insights in minutes.
PyData Seattle 2015
Alex Korbonits (@korbonits)
This presentation was given July 25, 2015 at the PyData Seattle conference hosted by PyData and NumFocus.
Effizientes Crawling für Websites. Anleitung um GoogleBot die Arbeit leichter zu machen. Besseres Ranking durch Crawl Budget Optimierung. Wichtige Hinweise zu Onsite SEO, Panda Diät, Panda Update, etc.
SEO-Vortrag SMX München 2016
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
The document discusses keyword research and strategies for understanding user intent. It provides tips for mapping keywords to user journeys and personas to gain deeper insights. Various tools for keyword research are also mentioned, including APIs that can be used to gather additional data without coding. Pivot charts and other visualizations are suggested to analyze keyword opportunities based on metrics like search volume and difficulty.
The document discusses Google's evolving approach to providing answers to user questions directly in search results through features like Knowledge Panels, Knowledge Graphs, and Featured Snippets. It notes how site content can be included in these answer features if it is highly relevant and ranked, and provides tips on keyword research and optimization to better target question-based queries and have content selected for the answer features.
Search engine optimization (SEO) is the process of affecting the visibility of a website or a web page in a search engine's "natural" or un-paid ("organic") search
Read all SEO Tips Shared by Matt Cutts in this PPT.
This document provides a summary of Michael King's presentation on the technical SEO renaissance. It discusses how SEO has evolved over time from basic tricks to a more technical focus as search engines have advanced. Key points include the growing importance of JavaScript, single page applications, HTTP headers, log file analysis, headless browsing, scraping techniques, content optimization using entities, internal linking structures, page speed optimizations, and preloading directives. The presentation argues that technical skills are now essential for SEOs to understand new developments and effectively optimize websites.
Fight Back Against Back: How Search Engines & Social Networks' AI Impacts Mar...Rand Fishkin
Rand's presentation on machine learning and deep learning in Google, Facebook, and beyond, and how engagement reputation will become key to every online marketing effort.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
Study on Impact of Media on Education Using Fuzzy Relational MapsMangaiK4
Abstract- In this paper we bring out the depth of impact of media upon the growth of education. Education moulds an individual to take firm decision on issues. It makes to feel independent and leads to a more exposed world. Media is a very powerful tool to explore the world and have access to the world. Internet, Mobile phones etc, helps for an easy access to any part of the world at out finger tips. Media may lead us to both constructive and destructive mechanism depending the way we deal with it. Here we use FRM model to study and analyze the impact of media on education.
This document proposes using spectral clustering based on the normalized graph Laplacian spectrum to solve problems in community detection and handwritten digit recognition. It summarizes the key concepts in graph signal processing and introduces spectral clustering. The paper provides a mathematical proof that the signs of the second eigenvector components of the normalized graph Laplacian can accurately partition a graph into two communities. It then applies this spectral clustering method to community detection and digit recognition, comparing results to other popular algorithms to demonstrate the advantages of the spectral clustering approach.
The document presents an algorithm for cooperative particle filtering for sensor network localization. It describes a distributed cooperative particle filter (CoopPF) that allows nodes to estimate their unknown locations by exploiting inter-node ranging measurements and communicating location probability distributions. The algorithm factorizes weight calculations to allow an iterative distributed implementation. It also proposes parametric distribution approximations to further reduce communication costs. Simulation results show the CoopPF and variants achieve accurate localization and perform better than existing methods in terms of mean square error over time and ranging noise levels.
This document provides an overview of mobile data offloading techniques for next generation cellular networks. It discusses the expected growth in mobile data traffic and need for offloading to WiFi networks. It presents a model for the offloading system involving mobile network operators, base stations and access points. It formulates the offloading problem as an optimization to maximize social welfare. An iterated double auction mechanism is proposed to solve the optimization in a distributed manner while achieving the desired economic properties. Results show the mechanism enables the requests and admissions to converge over iterations, minimizing the demand gap.
This document proposes methods for discovering organizational structure in static and dynamic social networks. For static networks, it introduces an m-Score to represent member importance and builds a community tree to represent the organizational hierarchy. For dynamic networks, it develops a tree learning algorithm to reconstruct the evolving community tree based on scoring past and current community structures. Experiments on karate club and Enron email networks demonstrate the approach.
A Study of the attitudes of road user in Enhancing the Gross National Happine...ijdmtaiir
This document summarizes a study that uses Combined Fuzzy Cognitive Maps (CFCMs) to analyze the attitudes of road users and how they impact gross national happiness. Ten attributes related to road use were identified by experts, including increased vehicle numbers, use of communication devices while driving, and disobeying traffic rules. Three experts each provided a connection matrix representing the causal relationships between attributes. The combined connection matrix was analyzed using initial state vectors, and a fixed point was reached where all attributes were in the "on" state, indicating increased vehicle numbers causes all other attributes to be active. The study concludes that obeying traffic rules and managing population growth can help create an accident-free society and enhance well-being and happiness.
The document discusses basic concepts related to continuous functions. It begins with an introduction and motivation for studying continuous functions. Some key reasons mentioned are that continuous functions are needed for integration and as underlying functions in differential equations. The document then provides definitions of limits and continuity in terms of limits. It gives examples of determining limits and continuity for various functions. Contributors to the field like Bolzano, Cauchy, and Weierstrass are also acknowledged. The document concludes with additional definitions of continuity, examples, and discussions of uniform continuity.
A Study of Pervasive Computing Environments in Improving the Quality of Life ...ijdmtaiir
This document discusses using induced linked fuzzy relational maps (ILFRMs) to analyze the impact of computer education on job opportunities and quality of life. It defines FRMs and LFRMs and the process of finding hidden patterns in ILFRMs. Attributes related to computer education impact, available jobs, and quality of life dimensions are identified. Expert opinions are used to construct the relational matrices between these attribute groups. The hidden pattern analysis process is described, taking the "increasing creative/tolerance power" attribute as the initial ON state. The analysis shows computer education can increase job opportunities which in turn improve quality of life dimensions.
Traveling Salesman Problem in Distributed Environmentcsandit
In this paper, we focus on developing parallel algorithms for solving the traveling salesman problem (TSP) based on Nicos Christofides algorithm released in 1976. The parallel algorithm
is built in the distributed environment with multi-processors (Master-Slave). The algorithm is installed on the computer cluster system of National University of Education in Hanoi,
Vietnam (ccs1.hnue.edu.vn) and uses the library PJ (Parallel Java). The results are evaluated and compared with other works.
TRAVELING SALESMAN PROBLEM IN DISTRIBUTED ENVIRONMENTcscpconf
The document describes developing a parallel algorithm for solving the traveling salesman problem (TSP) based on Christofides' algorithm. It discusses implementing Christofides' algorithm in a distributed environment using multiple processors. The parallel algorithm divides the graph vertices and distance matrix across slave processors, which calculate the minimum spanning tree in parallel. The master processor then finds odd-degree vertices, performs matching, and finds the Hamiltonian cycle to solve TSP. The algorithm is tested on a computer cluster using graphs of 20,000 and 30,000 nodes, showing improved runtime over the sequential algorithm.
PREDICTIVE EVALUATION OF THE STOCK PORTFOLIO PERFORMANCE USING FUZZY CMEANS A...ijfls
The aim of this paper is to investigate the trend of the return of a portfolio formed randomly or for any
specific technique. The approach is made using two techniques fuzzy: fuzzy c-means (FCM) algorithm and
the fuzzy transform, where the rules used at fuzzy transform arise from the application of the FCM
algorithm. The results show that the proposed methodology is able to predict the trend of the return of a
stock portfolio, as well as the tendency of the market index. Real data of the financial market are used from
2004 until 2007.
The document discusses the Fundamental Theorem of Calculus, which has two parts. Part 1 establishes the relationship between differentiation and integration, showing that the derivative of an antiderivative is the integrand. Part 2 allows evaluation of a definite integral by evaluating the antiderivative at the bounds. Examples are given of using both parts to evaluate definite integrals. The theorem unified differentiation and integration and was fundamental to the development of calculus.
A Study on Youth Violence and Aggression using DEMATEL with FCM Methodsijdmtaiir
The DEMATEL method is then a good technique for
making decisions. In this paper we analyzed the risk factors of
youth violence and what makes them more aggressive. Since
there are more risk factors of youth violence, to relate each
other more complex to construct FCM and analyze them.
Moreover the data is an unsupervised one obtained from
survey as well as interviews. Hence fuzzy alone has the
capacity to analyses these concepts.
This document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It begins with an introduction to fuzzy concepts in databases, including representing imprecise data using fuzzy sets and membership functions. It then defines a new measure for fuzzy equality that is used to define a fuzzy equi-join. The document proposes a sort-merge join algorithm that sorts relations based on a partial order of intervals to efficiently evaluate the fuzzy equi-join in two phases: sorting and joining. Experimental results are said to show a significant improvement in efficiency when using this algorithm.
This document discusses higher dimensional image analysis using concepts from convex geometry and mathematical morphology. It begins by defining types of images and morphological operators like dilation and erosion. It then links these concepts to convex analysis, discussing topics like morphological covers and the Brunn-Minkowski theorem. It proposes that convex structures can be extended to higher dimensional image analysis and defines the concept of a morphological space. The document concludes that convex sets play an important role in higher dimensional analysis and this work may help reduce difficulties in higher dimensional image processing.
1) The document discusses using big data and financial innovation from research to practice. It identifies challenges that traditional financial services face and opportunities that big data presents.
2) It analyzes the three main values of big data: insights from scale, knowledge from enrichment, and agility from real-time responsiveness. It also compares internal enterprise data and external social media big data.
3) The document provides examples of using big data for precision marketing and relationship marketing/risk management. It also discusses research topics like mining offline relationships from online social networks.
Mc0079 computer based optimization methods--phpapp02Rabby Bhatt
This document discusses mathematical models and provides examples of different types of mathematical models. It begins by defining a mathematical model as a description of a system using mathematical concepts and language. It then classifies mathematical models in several ways, such as linear vs nonlinear, deterministic vs probabilistic, static vs dynamic, discrete vs continuous, and deductive vs inductive vs floating. The document provides examples and explanations of each type of model. It also discusses using finite queuing tables to analyze queuing systems with a finite population size. In summary, the document outlines different ways to classify mathematical models and provides examples of applying various types of models.
This document discusses modeling networks using regression analysis with additive and multiplicative effects. It introduces network modeling and describes some common network regression models, including the social relations model (SRM) which captures sender and receiver effects. The document discusses incorporating covariates into these models and using multiplicative effects to better capture triadic behavior and homophily in networks. It also briefly mentions generalizing these models to ordinal outcomes.
MODELING SOCIAL GAUSS-MARKOV MOBILITY FOR OPPORTUNISTIC NETWORK csandit
Mobility is attracting more and more interests due to its importance for data forwarding
mechanisms in many networks such as mobile opportunistic network. In everyday life mobile
nodes are often carried by human. Thus, mobile nodes’ mobility pattern is inevitable affected by
human social character. This paper presents a novel mobility model (HNGM) which combines
social character and Gauss-Markov process together. The performance analysis on this
mobility model is given and one famous and widely used mobility model (RWP) is chosen to
make comparison..
Similar to Magnet community identification on social networks (20)
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
Magnet community identification on social networks
1. Guan Wang, Yuchen Zhao,
Xiaoxiao Shi, Philip S. Yu
Department of Computer Science
University of Illinois at ChicagoKDD 2012
2. Magnet Community
More Examples
The ones those attract people’s interests more than
their peers
myspace facebook
vs
Magnet school Magnet Conference Magnet Company
3. Attention Flow among Communities
http://blog.topprospect.com/2011/06/the-biggest-talent-losers-and-winners/
4. What makes a community magnet?
Attention : Its in-flow should be
larger than the out-flow
Attention : The in-flow comes from
other communities with high attractiveness
levels
Attention : Its first two
properties should be persistent
5. How to Identify Magnet Community
in Social Networks
Rule # 1:
PageRank does work……
Problem
The ranking screwed
towards large communities
Normalizing ranking
scores by community size
does not work either
More Challenges
ble 1 lists the results after normalization. The top
become tiny start-ups with about 100 employees
are the companies recognized as “ideal employe
survey result from Universumglobal 2
. It aligns b
common sense about IT industry. As we have se
dom walk schemes cannot accurately measure th
communities. Although the survey result can c
mance, it takes a lot of efforts and manual work
infeasible for large-scale identification tasks.
Rank PageRank Normalized PR
1 Hewlett Packard Zuora
2 IBM Silver Peak Systems
3 Oracle Kony Solutions
4 Microsoft Palo Alto Networks
5 Cisco Systems Quickoffice
Table 1: Top Ranked IT Compa
Therefore, the magnet community identificatio
lenging than it appears to be. First, there is no s
we could rely on to determine the attractivenes
“Top Ranked” IT Companies
6. More Challenges
: Information from
heterogeneous sources, such as node, edge…
:
Modeling the heterogeneity coherently with
attractiveness properties, including attention
flow, quality, and persistency
: The overall results must make
sense, although small portion of people prefer non-
magnet communities to magnet ones
8. Framework
Preliminary Definitions
is a vector of
magnetism, or attractiveness values, for each
community.
is the estimated
is the standalone feature vector for every
community
is the dependency feature vector
10. Framework
Attractiveness Features
Standalone Features
Dependency Features
▪ Where D is the attention transition matrix ( is how
much attention transfers from i to j)
▪ is the normalization matrix
▪ is the probabilistic transitional matrix,
(FE(i,j) is the fraction of attention that j draws
from i) It is the dependency feature
comes inactive in community i. Respectively, joining
erson becomes active in j . Note that what we assume
very person could only be active in one community at
vector A = (ai )k∗1 = D · ebe the attention vector,
k-by-1 unit vector. Thus, ai is the total number of
epart from community i. Let A be the element-wise
or of A, where A = (a− 1
i )k∗1. We have dependency
mmunities as FE = A ◦ DT
, which is the Hadamard
and D.
dency matrix, or edge feature, FE is a probabilistic
atrix 3
. Each column of FE is the distribution of peo-
entions are migrating to other communities.
crete formula of magnet community
king framework
be c
of in
cont
A
of th
i ov
cons
lowe
(i ,j
T
tured
ther. This unique relation is modeled as an attention
matrix D = (di j )k ∗ k , where di j is the actual number
who depart from community i and join j . Departing from
he person becomes inactive in community i. Respectivel
means the person becomes active in j . Note that what w
ere is that every person could only be active in one com
ne time. Let vector A = (ai )k ∗ 1 = D · e be the attentio
where e is a k-by-1 unit vector. Thus, ai is the total n
eople who depart from community i. Let A be the elem
nverted vector of A, where A = (a− 1
i )k ∗ 1. We have de
eatures of communities as FE = A ◦ D T
, which is the H
roduct of A and D .
The dependency matrix, or edge feature, FE is a pro
ansitional matrix 3
. Each column of FE is the distributio
le whose attentions are migrating to other communities.
ion away, so that they become active in somewhere
communities draw people’s attention among each
que relation is modeled as an attention migrating
i j )k ∗k , where di j is the actual number of people
m community i and join j . Departing from i means
mes inactive in community i. Respectively, joining
on becomes active in j . Note that what we assume
y person could only be active in one community at
ctor A = (ai )k ∗1 = D · e be the attention vector,
by-1 unit vector. Thus, ai is the total number of
art from community i. Let A be the element-wise
of A, where A = (a− 1
i )k ∗ 1. We have dependency
munities as FE = A ◦ DT
, which is the Hadamard
d D.
ncy matrix, or edge feature, FE is a probabilistic
ix 3
. Each column of FE is the distribution of peo-
be contr
of in-flo
contribu
Altho
of the ab
i over j
constrain
lower bo
11. Framework
Constraints
mi > mj when:
1. i’s attention flow is higher than j’s
2. i’s standalone feature is better than j’s
An example could be the employee
transferring case:
one jumps from companyA to company B,
either because B is promising or B provides
better salary
?
12. Framework
Optimization with constraints
and in (0,1) are weighting params
is the lower bound of the constraint
M = αFEM + (1 − α)FV , 0 ≤ α ≤ 1 (4)
where α is a weighting parameter. With that formula, we can
ewrite the objective function as
min ||M ∗
− M ||2
F (5)
= min ||αFE M + (1 − α)FV − M ||2
F (6)
= min ||(αFE − I )M + (1 − α)FV ||2
F (7)
Now let us focus on the constraint for the above objective function.
When we say one community is more magnetic than the other, at
east one of the following two conditions are very likely to happen.
First, this community has better standalone features. Second, it
raws people’s attention out of other similar communities. On the
ontrast, it is unlikely for a community to be more magnetic than
thers if it is inferior on both conditions. Formally, when i is more
magnetic than j , i.e., mi − mj > 0, we want at least one of the
ollowing conditions hold.
• f i > f j
where ni
i
ni
ou t is n
meaning
We org
Here, M
Now w
THEO
following
eople
means
ining
sume
ity at
ector,
er of
-wise
dency
mard
listic
peo-
nity
node
ctive-
other
Figure 3: Contribution imbalance
be contributed significantly than smaller ones with the same size
of in-flow (see Figure 3). Therefore, we call the second condition
contribution imbalance.
Although it is possible for people to move from i to j if only one
of the above conditions is true, it is very unlikely for them to prefer
i over j if none of the two condition is true. Thus, we make our
constraint as follows, where µ is a weighting parameter and ζ is a
lower bound.
(i ,j )
(mi − mj ) ∗ (µ(
dj i
Si
−
di j
Sj
) + (1 − µ)(f i − f j )) ≥ ζ (8)
Therefore, the three properties of magnet communities are cap-
tured into Eq. 7 and Eq. 8 in a subtle way. Eq. 7 states that a com-
munity would have better chance to be a magnet one if it attracts
attentions from other high magnet communities, which implies the
second property. Eq. 8 constraints the magnet computation results
must consistent with the first and third properties in Definition 2.3,
eature, FE is a probabilistic
F E is the distribution of peo-
other communities.
magnet community
s of a community, i.e., a node
estart. A node’s attractive-
of it being visited from other
ability that people’s attention
. Upon combining heteroge-
n GC, we have
)F V , 0 ≤ α ≤ 1 (4)
With that formula, we can
lower bound.
( i ,j )
(mi − mj ) ∗ (µ(
Therefore, the three p
tured into Eq. 7 and Eq.
munity would have bett
attentions from other hig
second property. Eq. 8 c
must consistent with the
which are reflected by th
We rewrite the constra
n
i = 1 u ∈ n i
i n
ontribution imbalance
than smaller ones with the same size
herefore, we call the second condition
people to move from i to j if only one
ue, it is very unlikely for them to prefer
condition is true. Thus, we make our
µ is a weighting parameter and ζ is a
−
di j
Sj
) + (1 − µ)(f i − f j )) ≥ ζ (8)
erties of magnet communities are cap-
ting parameter and ζ is a
− µ)(f i − f j )) ≥ ζ (8)
net communities are cap-
y. Eq. 7 states that a com-
magnet one if it attracts
unities, which implies the
agnet computation results
operties in Definition 2.3,
.
ng like terms as
v i mi ≥ ζ (9)
s in GC and
13. Framework
Equivalency to the following canonical
quadratic programming forms:
Q is positive definitive in this
case, which guarantees the solution only
costs polynomial time
ΦM ≥ ζ (10)
e, M is the vector of { mi } 1∗n and Φ is its coefficient vector.
ow we discuss how to solve the optimization framework.
HEOREM 1. Our optimization framework is equivalent to the
owing canonical quadratic programming form:
min M T
QM − 2uT
M (11)
s.t., H M ≤ ξ (12)
ROOF. The objective function of Eq. 7 can be rewritten as
||(αFE − I )M + (1 − α)FV ||2
F
(M T
(αFE
T
− I )(αFE − I )M + (1− α)M T
(αFE
T
− I )FV
+ (1 − α)FV
T
(αFE − I )M + (1 − α)2
FV
T
FV )
14. Experiments:
Data Sets
(1 − α)(αFE
T
− I )FV ,
ur optimization framework
olution of our optimization,
whether the global minimal
optimization.
ramework is positive defi-
is the eigenvalue of FE .
atrix, |FE | = 0. We have
= αFE X − X = (αλ −
E − I ) is αλ − 1. Thus,
αFE − I )T
(αFE − I ), Q
(a) Facebook employee
flow
(
p
Figure 4: Employee Migra
Standalone features (reuters.com,
linkedin.com)
Size
Location
Industry growth
Age
P/E ratio
Dependency features (linkedin.com)
16. Rank PageRank MIM Ideal Employer Admired Company
1 IBM Google Google Apple
2 Hewlett Packard Amazon.com Microsoft Google
3 Oracle Apple Apple Amazon.com
4 Microsoft Microsoft Facebook IBM
5 Cisco Systems Facebook IBM Qualcomm
6 Google Salesforce.com Electronics Arts Intel
7 Tata Consult. Services Cisco Systems Amazon Texas Instruments
8 Cognizant Tech. Solu. Juniper Networks Cisco Systems Cisco Systems
9 Dell Yahoo! Intel Adobe Systems
10 EMC Linkedin Sony Oracle
Table 3: Top 10 IT Companies
Rank PageRank MIM Ideal Employer Admired Company
1 J.P. Morgan Chase J.P. Morgan Chase Goldman Sachs US Bank
k PageRank MIM Ideal Employer Admired Company
IBM Google Google Apple
Hewlett Packard Amazon.com Microsoft Google
Oracle Apple Apple Amazon.com
Microsoft Microsoft Facebook IBM
Cisco Systems Facebook IBM Qualcomm
Google Salesforce.com Electronics Arts Intel
Tata Consult. Services Cisco Systems Amazon Texas Instruments
Cognizant Tech. Solu. Juniper Networks Cisco Systems Cisco Systems
Dell Yahoo! Intel Adobe Systems
EMC Linkedin Sony Oracle
Table 3: Top 10 IT Companies
17. 4 Microsoft Microsoft Facebook IBM
5 Cisco Systems Facebook IBM Qualcomm
6 Google Salesforce.com Electronics Arts Intel
7 Tata Consult. Services Cisco Systems Amazon Texas Instruments
8 Cognizant Tech. Solu. Juniper Networks Cisco Systems Cisco Systems
9 Dell Yahoo! Intel Adobe Systems
10 EMC Linkedin Sony Oracle
Table 3: Top 10 IT Companies
Rank PageRank MIM Ideal Employer Admired Company
1 J.P. Morgan Chase J.P. Morgan Chase Goldman Sachs US Bank
2 Citigroup Goldman Sachs J.P. Morgan Chase Goldman Sachs
3 HSBC Morgan Stanley Boston Consult. Grp. J.P. Morgan Chase
4 PWC Citigroup Deloitte Merrill Lynch
5 Merrill Lynch Merrill Lynch Merrill Lynch Northern Trust Corp.
6 Ernst & Young CB Richard Ellis Ernst & Young Credit Suisse
7 Deutsche Bank Wells Fargo Morgan Stanley CB Richard Eills
8 Credit Suisse PWC PWC HSBC
9 Barclays Capital Jones Lang LaSalle American Express Barclays
10 Goldman Sachs Blackrock Bain & Company Jones Lang LaSalle
Table 4: Top 10 Finance Companies
chs are relatively unscathed by the recent company data in IT industry. As it shows
4 Microsoft Microsoft Facebook IBM
5 Cisco Systems Facebook IBM Qualcomm
6 Google Salesforce.com Electronics Arts Intel
7 Tata Consult. Services Cisco Systems Amazon Texas Instruments
8 Cognizant Tech. Solu. Juniper Networks Cisco Systems Cisco Systems
9 Dell Yahoo! Intel Adobe Systems
10 EMC Linkedin Sony Oracle
Table 3: Top 10 IT Companies
Rank PageRank MIM Ideal Employer Admired Company
1 J.P. Morgan Chase J.P. Morgan Chase Goldman Sachs US Bank
2 Citigroup Goldman Sachs J.P. Morgan Chase Goldman Sachs
3 HSBC Morgan Stanley Boston Consult. Grp. J.P. Morgan Chase
4 PWC Citigroup Deloitte Merrill Lynch
5 Merrill Lynch Merrill Lynch Merrill Lynch Northern Trust Corp.
6 Ernst & Young CB Richard Ellis Ernst & Young Credit Suisse
7 Deutsche Bank Wells Fargo Morgan Stanley CB Richard Eills
8 Credit Suisse PWC PWC HSBC
9 Barclays Capital Jones Lang LaSalle American Express Barclays
10 Goldman Sachs Blackrock Bain & Company Jones Lang LaSalle
Table 4: Top 10 Finance Companies
18. Discount Cumulative Gain: (bigger the better)
Widely used in IR to evaluate search engines
A measure on how reasonable a ranking is
Its value is higher when an entity is ranked higher
if it should be ranked higher
Average Weighted Distance: (smaller the better)
A measure on how far a ranking is from the ground truth
Its value is smaller when an entity is ranked higher
if it should be ranked higher
It cares more on the top ranked entities
19. (a) DCG on IT Ideal Employers (b) DCG on IT Admired Companies (c) Av
Figure 5: Performance on IT Indus
(a) DCG on IT Ideal Employers (b) DCG on IT Admired Companies (c) Av
Figure 5: Performance on IT Indus
(a) DCG on IT Ideal Employers (b) DCG on IT Admired Companies (c) Averag
Figure 5: Performance on IT Industry
(a) DCG on Finance Ideal Employers (b) DCG on Finance Admired Corp. (c) Avg W
(a) DCG on IT Ideal Employers (b) DCG on IT Admired Companies (c) Ave
Figure 5: Performance on IT Industr
(a) DCG on Finance Ideal Employers (b) DCG on Finance Admired Corp. (c) Av
Discount Cumulative Gain: (bigger the better)
20. es (c) Average Weighted Distance on IT
n IT Industry
(b) DCG on IT Admired Companies (c) Average Weighted Distance on
Figure 5: Performance on IT Industry
ers (b) DCG on Finance Admired Corp. (c) Avg Weighted Dist. on Financ
Figure 6: Performance on Finance Industry
nies (c) Average Weighted Distance on IT
on IT Industry
yers (b) DCG on IT Admired Companies (c) Average Weighted Distance on IT
Figure 5: Performance on IT Industry
ployers (b) DCG on Finance Admired Corp. (c) Avg Weighted Dist. on Finance
Figure 6: Performance on Finance Industry
• Average weighted distance: (smaller the better)
21. • Average Precision at cut-off K
e on high ranked entities, in addition to weighted distance,
measure the model’s performance on average precision
n
k = 1 P(k)∆ R(k), where P(k) is the precision at cut-
∆ R(k) is the change of recall from position k − 1 to k.
er normalize wDist using nwD ist = 1
Z
wDist, where
ormalization factor to make it align in the same scale as
Figure 7 we can see that our model performs consistently
ent α values. The fluctuations are in a small range. We
rve that the best performances are achieved at α = 0.6.
are achieved simul
and 0.5 to α and µ
to them for the sam
4. RELATE
Network comm
for a long time. Ho
dynamic communi
lution. To our bes
related to magnet c
Initially, people
the structural prop
connection densiti
tection has been d
addressing dynami
random walks to id
could also rank com
their method also
captured the chang
(a) EP and nwDist on α
the two parameters. Due to space limitation, we only show the
results on IT industry data and using admired company list as com-
parison. (Financial industry data give similar results.) Since we
care more on high ranked entities, in addition to weighted distance,
we also measure the model’s performance on average precision
EP = n
k= 1 P(k)∆ R(k), where P(k) is the precision at cut-
off k and ∆ R(k) is the change of recall from position k − 1 to k.
We further normalize wDist using nwDist = 1
Z
wDist, where
Z is a normalization factor to make it align in the same scale as
EP. In Figure 7 we can see that our model performs consistently
on different α values. The fluctuations are in a small range. We
also observe that the best performances are achieved at α = 0.6.
• Normalized Weighted Distance
o space limitation, we only show the
nd using admired company list as com-
y data give similar results.) Since we
tities, in addition to weighted distance,
l’s performance on average precision
, where P(k) is the precision at cut-
nge of recall from position k − 1 to k.
st using nwDist = 1
Z
wDist, where
to make it align in the same scale as
e that our model performs consistently
fluctuations are in a small range. We
rformances are achieved at α = 0.6.
Figure 7 also shows that µ
performance varies on differe
small range. The highest ave
are achieved simultaneously a
and 0.5 to α and µ to generate
to them for the same reason fo
4. RELATED WOR
Network community analy
for a long time. However, prev
dynamic community detectio
lution. To our best knowledg
related to magnet community
Initially, people paid great a
the structural properties of c
22. Conclusions
Magnet community identification is a new
direction
many application cases on social networks
The optimization framework is more suitable for the
problem than PageRank variations
It is also adaptable to different applications due to the
flexibility of defining constraints
Thank you!
Future works
other data and applications
▪ Magnet community with time evolving
Editor's Notes
Myspace is not as attractable as Facebook nowadays
We can treat each community as a “node” and interactions among communities as “edges”
Attractiveness, or magnetism level, or a community, depends on the its node feature, edge feature, and attractiveness value of other communities.M_star represents the estimated attractiveness value vector, while M represents the real, or theoretical attractiveness value. Our goal is to make the gap between them as small as possible.The whole optimization should have a bunch of constraints, which we will talk about later.
Standalone features are the ones thatonly depend on the community itself.Dependency features are the ones that depend on other communities.D is the raw attention flow matrixA is a normalizing matrix that can change D into a stochastic matrix, a Markov transition matrix
We want to minimize the gap between M* and M, which can be rewritten as the above form.The constraint means that if a community’s magnetism score is higher than the other, then two situations are likely to happen: 1. its attention flow is higher 2. its standalone feature is betterAn example could be the employee transferring case: one jumps from company A to company B, either because B is promising or B provides better salary