ONLINE SUBGRAPH SKYLINE ANALYSIS OVER KNOWLEDGE GRAPHS

•

0 likes•153 views

TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.

Education

CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
ONLINE SUBGRAPH SKYLINE ANALYSIS OVER KNOWLEDGE GRAPHS.
Abstract:
Subgraph search is very useful in many real-world applications. However, users
may be overwhelmed by the masses of matches. In this paper, we propose a
subgraph skyline analysis problem, denoted as S 2A, to support more
complicated analysis over graph data. Specifically, given a large graph G and a
query graph q, we want to find all the subgraphs g in G, such that g is graph
isomorphic to q and not dominated by any other subgraphs. In order to
improve the efficiency, we devise a hybrid feature encoding incorporating both
structural and numeric features based on a partitioning strategy, and discuss
how to optimize the space partitioning. We also present a skylayer index to
facilitate the dynamic subgraph skyline computation. Moreover, an attribute
cluster-based method is proposed to deal with the curse of dimensionality.
Extensive experiments over real datasets confirm the effectiveness and
efficiency of our algorithm.
CONCLUSION AND FUTURE WORK
In this paper, we formalize the problem of subgraph skyline analysis (denoted
as S 2A) over large graphs and propose an efficient algorithm to answer S 2A
queries. To improve the efficiency, we propose to partition the data space into
grid cells, based on which we carefully design feature encoding to facilitate the
query process. In order to handle the curse of dimensionality, we propose an
attribute cluster-based method. The experimental results on real datasets

This document proposes a technique called routing proxies to speed up shortest path and distance queries on large graphs. Routing proxies represent small subgraphs called deterministic routing areas (DRAs). The technique first computes routing proxies and their corresponding DRAs in linear time. It then shows that routing proxies reduce graph sizes and improve the efficiency of shortest path algorithms like Dijkstra's algorithm. Future work includes extending the technique to dynamic graphs and other types of graph queries.

K NEAREST NEIGHBOUR JOINS FOR BIG DATA ON MAPREDUCE: A THEORETICAL AND EXPER...

Nexgen Technology

Graph based Clustering

怡秀林

Graph-based clustering uses similarity graphs instead of feature vectors, representing data as a graph where nodes are data points and edges represent similarity. It can handle any data type as long as similarities are defined, and is useful for high-dimensional data by reducing dimensionality. However, computing similarities between all pairs of data points is computationally expensive. Survey papers discussed algorithms like spectral clustering and modularity optimization. Applications include document analysis, social networks, and recommendation systems. Future work includes determining optimal cluster numbers, scaling to large data, and handling directed graphs.

Data mining algorithm for cloud network information based on artificial intel...

Shakas Technologies

Poster Final

Gireeshma Reddy

This document summarizes a student's research project on approximate matching on graph databases using the GeX approach. It introduces graph databases and the need for approximate matching. It describes testing the GeX Top-K query algorithm on biological interaction data from multiple organisms. While accurate, the algorithm's performance decreases with larger datasets. Future work could approximate edge labels as well to improve scalability.

Fast and scalable range query processing with strong privacy protection for c...

Shakas Technologies

Networking Domain title 2016-2017 Shakas Technologies Provides, 2016-2017 Project titles, Real time project titles, IEEE Project Titles 2016-2017, NON-IEEE Project Titles, Latest project titles 2016, MCA Project titles, Final Year Project titles 2016-2017.. Contact As: Shakas Technologies #13/19, 1st Floor, Municipal Colony, Kangeyanellore Road, Gandhi Nagar, Vellore-632006, Cell: 9500218218 Mobile: 0416-2247353 / 6066663

sexy maps comes to R - ggplot+ google maps= ggmap #rstats

Ajay Ohri

ggmap is an R package that enables the visualization of spatial data superimposed on static maps from sources like Google Maps and OpenStreetMap. It combines these map images with the layered grammar of graphics implementation in ggplot2. This allows spatial data and models to be easily plotted on top of map backgrounds for contextualization. Key features include downloading maps via the get_map function and then plotting with ggmap using the consistent scales and conventions of ggplot2. The package facilitates the interpretation of spatial analyses and questions by non-experts through these informative visualizations.

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

ivaderivader

This paper proposes DMVST-Net, a deep learning framework that uses three views (spatial, temporal, and semantic) to predict taxi demand. It uses a local CNN to model spatial relationships between nearby regions, an LSTM to model temporal patterns over time, and region embeddings to model semantic relationships between spatially distant but correlated regions. An experiment on a large Didi Chuxing taxi dataset showed DMVST-Net outperformed other methods at predicting future demand, demonstrating the benefit of jointly modeling spatial, temporal, and semantic relationships.

Data conflation is a process that optimizes spatial data quality by automatically merging heterogeneous geospatial datasets. It detects corresponding real-world features represented differently across datasets and combines their geometric and semantic information. For example, conflating datasets can identify a traffic roundabout modeled inconsistently and insert it accurately. By integrating multiple sources, conflation improves data completeness, positional accuracy, and temporal accuracy to produce an optimized dataset for applications.

Improvement of Spatial Data Quality Using the Data Conﬂation

Beniamino Murgante

Multivariate visibility graphs for fMRI data

danielemarinazzo

This document discusses multivariate visibility graphs, which extend visibility graph analysis from univariate to multivariate time series analysis. Visibility graphs capture the visibility relationships between points in time series data and have been applied to analyze financial, climate, and fMRI brain data. The authors generalize this approach to analyze correlations between multiple time series through a multiplex network representation. They demonstrate the method on coupled map systems and multivariate financial data, finding it captures dynamical regimes and correlated behavior. They also apply it to a large fMRI dataset to analyze dynamic functional connectivity between brain regions.

Stochastic kronecker graphs

Zara Tariq

The document discusses stochastic Kronecker graphs (SKG), which are proposed as an efficient technique for modeling large-scale real-world networks. SKG uses Kronecker products of probability matrices to generate graphs that capture properties of natural networks, such as social networks and the web. It is hypothesized that SKG provides an efficient approach to graph mapping and generation in terms of speed and implementation for large networks. The conclusion reiterates that SKG algorithms can generate graphs with desired properties and are useful for analyzing complex systems represented as graphs.

K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH

Nexgen Technology

Skyline Query Processing using Filtering in Distributed Environment

IJMER

This document summarizes a research paper about skyline query processing in distributed databases. Skyline queries return multidimensional data points that are not dominated by other points. In distributed databases, skyline queries must be processed across multiple data sites. The paper proposes using multiple filtering points selected from each local skyline result to reduce the number of false positive results and communication costs between sites. Two heuristics called MaxSum and MaxDist are described for selecting filtering points that maximize their combined dominating potential across sites to improve distributed skyline query processing performance.

Ranking spatial data by quality preferences ppt

Saurav Kumar

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters

Clustering big spatiotemporal interval data

Nexgen Technology

SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATION

Nexgen Technology

Spin with cluster for data centric wireless

Shakas Technologies

This document proposes a clustering algorithm for efficiently transmitting data in wireless sensor networks. The algorithm is designed for use in battlefield scenarios, where sensors are uniformly distributed across a geographic region. It uses clustering to send data between different regions to an important base station. Depending on events, the appropriate clustering algorithm is used. The proposed scheme selects cluster heads in a way that provides better data gathering, stability, and extended network lifetime compared to the LEACH protocol. Simulations in NS2.34 show the proposed protocol has performance gains over LEACH in terms of network lifetime and guaranteed data transmission.

Ranking Preferences to Data by Using R-Trees

IOSR Journals

This document discusses algorithms for efficiently processing top-k spatial preference queries in databases containing spatial and non-spatial data. It defines top-k spatial preference queries as ranking objects based on quality and features in their nearest neighborhoods. It presents the branch and bound and feature join algorithms for computing the top-k results without having to calculate scores for all objects. It also discusses using R-trees to index spatial data and feature data to accelerate query processing.

Spatio temporal mining to identify potential traffic congestion based on tran...

Irrevaldy -

This document proposes a method to identify potential traffic congestion based on transportation mode using spatio-temporal mining of GPS data. It divides an area into grids, clusters GPS trajectory points from different time intervals to determine grid density levels, and overlays the clusters to identify areas with consistently high densities that indicate potential for congestion. The authors develop an architecture based on previous research that adds spatial approaches like gridding and clustering overlays to accommodate identifying congested areas. The goal is to analyze how transportation modes fill space over time to determine where congestion regularly occurs.

Graph based Semi Supervised Learning V1

Neeta Pande

This document summarizes graph-based semi-supervised learning techniques. It discusses how these techniques can effectively propagate labels from a small set of labeled data to large amounts of unlabeled data. Graph-based techniques are well-suited for real-world data that exists in graph form and can represent multi-modal, multi-format data sources. The document outlines key concepts like representing instances as graph vertices and connections as edges. It also describes popular graph-based SSL algorithms and challenges like dealing with large-scale data and noisy labels. Finally, it proposes a novel technique using hypergraph heat diffusion and summarizes the components and pipeline of this approach.

Distributed web systems performance forecasting

IEEEFINALYEARPROJECTS

Big Data? Big Issues: Degradation in Longitudinal Data and Implications for ...

mwe400

Secure spin with cluster for data centric wireless sensor network

Shakas Technologies

1) The document proposes a routing algorithm called Secure-SPIN for efficient data transmission in wireless sensor networks, especially for use in battlefield scenarios. 2) It uses clustering algorithms to send sensor data from different geographic regions to a base station, with cluster heads selected based on the event being monitored. 3) Simulation results show the proposed protocol performs better than LEACH in terms of network lifetime and guaranteed data transmission.

Funciones

bryan789

THE LOGO publishingurbanwordz.comUrban Voodoo

Photo caption sbc3_frewell partysinghabizcourse

Geralnews14mar

geralinvestimentos

What's hot

Conversation with-search-engines (Ren et al. 2020)

Vaclav Kosar

Correlations, Trends, and Outliers in ggplot2

Chris Rucker

Iccsa stankuteha180611

Beniamino Murgante

Improvement of Spatial Data Quality Using the Data Conﬂation

Beniamino Murgante

Multivariate visibility graphs for fMRI data

danielemarinazzo

Stochastic kronecker graphs

Zara Tariq

K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH

Nexgen Technology

Skyline Query Processing using Filtering in Distributed Environment

IJMER

Ranking spatial data by quality preferences ppt

Saurav Kumar

Clustering big spatiotemporal interval data

Nexgen Technology

SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATION

Nexgen Technology

Spin with cluster for data centric wireless

Shakas Technologies

Ranking Preferences to Data by Using R-Trees

IOSR Journals

Spatio temporal mining to identify potential traffic congestion based on tran...

Irrevaldy -

Graph based Semi Supervised Learning V1

Neeta Pande

Distributed web systems performance forecasting

IEEEFINALYEARPROJECTS

Big Data? Big Issues: Degradation in Longitudinal Data and Implications for ...

mwe400

Secure spin with cluster for data centric wireless sensor network

Shakas Technologies

What's hot (18)

Conversation with-search-engines (Ren et al. 2020)

Correlations, Trends, and Outliers in ggplot2

Iccsa stankuteha180611

Improvement of Spatial Data Quality Using the Data Conﬂation

Multivariate visibility graphs for fMRI data

Stochastic kronecker graphs

K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH

Skyline Query Processing using Filtering in Distributed Environment

Ranking spatial data by quality preferences ppt

Clustering big spatiotemporal interval data

SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATION

Spin with cluster for data centric wireless

Ranking Preferences to Data by Using R-Trees

Spatio temporal mining to identify potential traffic congestion based on tran...

Graph based Semi Supervised Learning V1

Distributed web systems performance forecasting

Big Data? Big Issues: Degradation in Longitudinal Data and Implications for ...

Secure spin with cluster for data centric wireless sensor network

Viewers also liked

Funciones

bryan789

THE LOGO publishingurbanwordz.comUrban Voodoo

Photo caption sbc3_frewell partysinghabizcourse

Geralnews14mar

geralinvestimentos

BRD-001-Giant的全球行銷策略與品牌handbook

Presentación

Angel Diaz Torres

Icen advies festivak 2015 technologie

Festivak

Democracia participativa y protagonicacompi_82

CV David Ortega

unionprogresodemocracia

Sistema Endócrino e Aparello Locomotor

irenetraba

創業管理創業型大學星光班-詹翔霖教授-零售行銷通路

翔霖詹

Oefenzitting 5 asteroide affichepierrevc

Slide 1 (shared using http://VisualBee.com).VisualBee.com

Antologia chistes

k-on

11th grade english unit 11.4 week 4

Efraín Suárez-Arce, M.Ed

This document outlines steps for a student assignment to compare and contrast how detectives solved mysteries in two stories - the book "Encyclopedia Brown and the Case of the Slippery Salamander" and the video "Sherlock Holmes: The Dying Detective". The student will write journal entries about each mystery, draft an expository essay comparing the problem-solving processes, edit their work, present it orally to the class, and have it assessed using a rubric.

Inmunosupresion en trasplante renal @DokRenal

Igor Romaniuk

Este documento resume los principales fármacos utilizados en la inmunosupresión para trasplante renal, incluyendo corticosteroides, ciclosporina, tacrolimus, azatioprina, rapamicina y micofenolato de mofetil. Describe sus mecanismos de acción, dosis, efectos adversos y comparaciones entre fármacos para lograr una adecuada inmunosupresión y prevenir el rechazo del injerto renal tras el trasplante.

HR-053-IBM HRhandbook

Manual para editar videos

metodologiauvas

Las tic como herramientas indispensables para el desarrollo social y cultural

Francy Gamez

El documento discute la importancia de las TIC para el desarrollo social y cultural. Explica que históricamente la ciencia ha estado reservada para grupos privilegiados, pero que las TIC permiten una difusión casi ilimitada del conocimiento científico. También destaca que las TIC son herramientas indispensables para mejorar la calidad de vida en áreas como la educación, la salud y la cultura. Finalmente, argumenta que las políticas de ciencia, tecnología e innovación deben asegurar que las TIC estén al alcance de todos

Direitos autoras

rayan123773

Direitos autorais são leis que garantem a circulação e veiculação de produções culturais, permitindo que artistas recuperem investimentos e garantam créditos. A internet exigiu novas licenças para regulamentar a distribuição digital de obras. Atualmente, os direitos autorais musicais no Brasil são regidos pela Lei 9.610/98, que protege criadores e define deveres de quem usa obras musicais.

Viewers also liked (20)

Funciones

THE LOGO publishingurbanwordz.com

Photo caption sbc3_frewell party

Geralnews14mar

BRD-001-Giant的全球行銷策略與品牌

Presentación

Icen advies festivak 2015 technologie

Democracia participativa y protagonica

CV David Ortega

Sistema Endócrino e Aparello Locomotor

創業管理創業型大學星光班-詹翔霖教授-零售行銷通路

Oefenzitting 5 asteroide affiche

Slide 1 (shared using http://VisualBee.com).

Antologia chistes

11th grade english unit 11.4 week 4

Inmunosupresion en trasplante renal @DokRenal

HR-053-IBM HR

Manual para editar videos

Las tic como herramientas indispensables para el desarrollo social y cultural

Direitos autoras

Similar to ONLINE SUBGRAPH SKYLINE ANALYSIS OVER KNOWLEDGE GRAPHS

A hybrid approach to clustering in big data

Nexgen Technology

Introducing Novel Graph Database Cloud Computing For Efficient Data Management

IJERA Editor

Graph theory stands as a natural mathematical model for cloud networks, axiomatic cloud theory further defines the cloud with formal mathematical model. keeping axiomatic theory as a basis, paper proposes bipartite cloud and proposes graph database model as a suitable database for data management .it is highlighted that perfect matching in bipartite cloud can enhance searching in bipartite cloud.

Sub-Graph Finding Information over Nebula Networks

ijceronline

Social and information networks have been extensively studied over years. This paper studies a new query on sub graph search on heterogeneous networks. Given an uncertain network of N objects, where each object is associated with a network to an underlying critical problem of discovering, top-k sub graphs of entities with rare and surprising associations returns k objects such that the expected matching sub graph queries efficiently involves, Compute all matching sub graphs which satisfy "Nebula computing requests" and this query is useful in ranking such results based on the rarity and the interestingness of the associations among nebula requests in the sub graphs. "In evaluating Top k-selection queries, "we compute information nebula using a global structural context similarity, and our similarity measure is independent of connection sub graphs". We need to compute the previous work on the matching problem can be harnessed for expected best for a naive ranking after matching for large graphs. Top k-selection sets and search for the optimal selection set with the large graphs; sub graphs may have enormous number of matches. In this paper, we identify several important properties of top-k selection queries, We propose novel top–K mechanisms to exploit these indexes for answering interesting sub graph queries efficiently.

[20240513_LabSeminar_Huy]GraphFewShort_Transfer.pptx

thanhdowork

Improving viability of electric taxis by taxi service strategy optimization a...

nexgentechnology

This document discusses improving the viability of electric taxis through optimization of taxi service strategies using big data from New York City taxi trips. It proposes using a Markov decision process to model an optimized computerized taxi service strategy. The profitability of electric taxis is empirically studied under different battery capacities and charging conditions. The optimal strategy from the Markov decision process can be implemented in an intelligent recommender system to help electric taxi drivers earn comparable revenues to those using internal combustion engine taxis.

Learning Graph Representation for Data-Efficiency RL

lauratoni4

This document provides information about Laura Toni's presentation on learning graph representation for data-efficient reinforcement learning. It discusses Laura Toni's affiliation with the LASP Research group at University College London, which focuses on machine learning, signal processing, and developing strategies for large-scale networks exploiting graph structures. The key goal is to exploit graph structure to develop efficient learning algorithms. The document lists some applications such as virtual reality systems, bandit problems, structural reinforcement learning, and influence maximization.

Laplacian-regularized Graph Bandits

lauratoni4

This document summarizes the key points of a research paper on regularized graph convolutional neural networks (RGCNN) for point cloud segmentation. Specifically: 1) RGCNN directly processes raw point clouds without voxelization or other preprocessing. It constructs graphs based on point coordinates and normals, performs graph convolutions to learn features, and adaptively updates the graphs during learning. 2) RGCNN leverages spectral graph theory to treat point cloud features as graph signals, defines convolutions via Chebyshev polynomial approximation, and regularizes learning with a graph-signal smoothness prior. 3) Experiments on ShapeNet show RGCNN achieves competitive segmentation performance with lower complexity than state-of-the

PointNet

PetteriTeikariPhD

Journal club done with Vid Stojevic for PointNet: https://arxiv.org/abs/1612.00593 https://github.com/charlesq34/pointnet http://stanford.edu/~rqi/pointnet/ Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing. Alternative download link: https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0

RESOLVING MULTI-PARTY PRIVACY CONFLICTS IN SOCIAL MEDIA

Nexgen Technology

240325_JW_labseminar[node2vec: Scalable Feature Learning for Networks].pptx

thanhdowork

This document describes the node2vec algorithm for feature learning in networks. Node2vec uses random walks to sample the neighborhood of nodes in a network. It learns feature representations that maximize the likelihood of preserving network neighborhoods in a low-dimensional space. The algorithm introduces two parameters, p and q, that allow it to flexibly explore node neighborhoods. Experiments on real-world networks show node2vec produces high quality feature representations that achieve strong performance on tasks like multi-label classification and link prediction.

Biologically inspired deep residual networks

IAESIJAI

Many difficult computer vision issues have been effectively tackled by deep neural networks. Not only that but it was discovered that traditional residual neural networks (ResNet) captures features with high generalizability, rendering it a cutting-edge convolutional neural network (CNN). The images classified by the authors of this research introduce a deep residual neural network that is biologically inspired introduces hexagonal convolutions along the skip connection. With the competitive training techniques, the effectiveness of several ResNet variations using square and hexagonal convolution is assessed. Using the hex-convolution on skip connection, we designed a family of ResNet architecture, hexagonal residual neural network (HexResNet), which achieves the highest testing accuracy of 94.02%, and 55.71% on Canadian Institute For Advanced Research (CIFAR)-10 and TinyImageNet, respectively. We demonstrate that the suggested method improves vanilla ResNet architectures’ baseline image classification accuracy on the CIFAR-10 dataset, and a similar effect was seen on the TinyImageNet dataset. For Tiny- ImageNet and CIFAR-10, we saw an average increase in accuracy of 1.46% and 0.48% in the baseline Top-1 accuracy, respectively. The generalized performance of advancements was reported for the suggested bioinspired deep residual networks. This represents an area that might be explored more extensively in the future to enhance all the discriminative power of image classification systems.

1809.05680.pdf

AnkitBiswas31

The document describes a new multi-vehicle trajectory generator (MTG) that can generate realistic vehicle encounter scenarios by encoding real encounter data into an interpretable latent space representation. The MTG uses a bi-directional encoder with GRU modules to extract temporal features from encounter trajectories, and a multi-branch decoder to separately generate multiple vehicle trajectories while sharing hidden states. A new disentanglement metric is proposed to evaluate deep generative models based on the variance of latent codes under different noise levels, without requiring classifiers. The MTG is shown to generate more rational vehicle encounters than baseline models and allow controlling encounters through the disentangled latent codes.

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

ivaderivader

This document summarizes a research paper on learning graph representations for deep divergence graph kernels (DDGK). DDGK learns graph representations without supervision or domain knowledge by using a node-to-edges encoder and isomorphism attention. The isomorphism attention provides a bidirectional mapping between nodes in two graphs. DDGK then calculates a divergence score between the source and target graphs as a measure of their (dis)similarity. Experimental results showed DDGK produces representations competitive with other graph kernel baselines. The paper proposes several extensions, including different graph encoders and attention mechanisms, as well as improved regularization and scalability.

Locally densest subgraph discovery

aftab alam

The document summarizes research on locally densest subgraph discovery. It discusses limitations of prior work that focuses on finding only the single densest subgraph or top-k dense subgraphs through a greedy approach. This may fail to fully characterize the graph's dense regions. The paper proposes defining a locally densest subgraph as one that is maximally ρ-compact, meaning it is connected and removal of nodes removes at least ρ times as many edges, ensuring it is not contained within a better subgraph. This formal definition can better represent different dense regions for applications like community detection.

What Makes Graph Queries Difficult?

Gábor Szárnyas

Graph queries and analytics pose several challenges. Graphs have an unstructured, connected nature that makes them difficult for computers to process efficiently. This is due to poor cache locality and difficulties in parallelization. Adding properties, types, weights, or global queries further increases complexity. There is also no consensus on a unified theory for graph processing, between relational algebra and linear algebra approaches. The speaker's PhD dissertation aims to address these challenges through contributions across different domains including databases, high-performance computing, network science, and software engineering.

Node classification with graph neural network based centrality measures and f...

IJECEIAES

Graph neural networks (GNNs) are a new topic of research in data science where data structure graphs are used as important components for developing and training neural networks. GNN always learns the weight importance of the neighbor for perform message aggregation in which the feature vectors of all neighbors are aggregated without considering whether the features are useful or not. Using such more informative features positively affect the performance of the GNN model. So, in this paper i) after selecting a subset of features to define important node features, we present new graph features’ explanation methods based on graph centrality measures to capture rich information and determine the most important node in a network. Through our experiments, we find that selecting certain subsets of these features and adding other features based on centrality measure can lead to better performance across a variety of datasets and ii) we introduce a major design strategy for graph neural networks. Specifically, we suggest using batch renormalization as normalization over GNN layers. Combining these techniques, representing features based on centrality measures that passed to multilayer perceptron (MLP) layer which is then passed to adjusted GNN layer, the proposed model achieves greater accuracy than modern GNN models.

Massive parallelism with gpus for centrality ranking in complex networks

ijcsit

Many problems in Computer Science can be modelled using graphs. Evaluating node centrality in complex networks, which can be considered equivalent to undirected graphs, provides an useful metric of the relative importance of each node inside the evaluated network. The knowledge on which the most central nodes are, has various applications, such as improving information spreading in diffusion networks. In this case, most central nodes can be considered to have higher influence rates over other nodes in the network. The main purpose in this work is developing a GPU based and massively parallel application so as to evaluate the node centrality in complex networks using the Nvidia CUDA programming model. The main contribution of this work is the strategies for the development of an algorithm to evaluate the node centrality in complex networks using Nvidia CUDA parallel programming model. We show that the strategies improves algorithm´s speed-up in two orders of magnitude on one NVIDIA Tesla k20 GPU cluster node, when compared to the hybrid OpenMP/MPI algorithm version, running in the same cluster, with 4 nodes 2 Intel(R) Xeon(R) CPU E5-2660 each, for radius zero

EFFICIENT R-TREE BASED INDEXING SCHEME FOR SERVER-CENTRIC CLOUD STORAGE SYSTEM

Nexgen Technology

Federated learning based_trafiic_flow_prediction.ppt

khalidhassan105

CAN SENSORS COLLECT BIG DATA? AN ENERGY EFFICIENT BIG DATA GATHERING ALGORIT...

Nexgen Technology

Similar to ONLINE SUBGRAPH SKYLINE ANALYSIS OVER KNOWLEDGE GRAPHS (20)

A hybrid approach to clustering in big data

Introducing Novel Graph Database Cloud Computing For Efficient Data Management

Sub-Graph Finding Information over Nebula Networks

[20240513_LabSeminar_Huy]GraphFewShort_Transfer.pptx

Improving viability of electric taxis by taxi service strategy optimization a...

Learning Graph Representation for Data-Efficiency RL

Laplacian-regularized Graph Bandits

PointNet

RESOLVING MULTI-PARTY PRIVACY CONFLICTS IN SOCIAL MEDIA

240325_JW_labseminar[node2vec: Scalable Feature Learning for Networks].pptx

Biologically inspired deep residual networks

1809.05680.pdf

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

Locally densest subgraph discovery

What Makes Graph Queries Difficult?

Node classification with graph neural network based centrality measures and f...

Massive parallelism with gpus for centrality ranking in complex networks

EFFICIENT R-TREE BASED INDEXING SCHEME FOR SERVER-CENTRIC CLOUD STORAGE SYSTEM

Federated learning based_trafiic_flow_prediction.ppt

CAN SENSORS COLLECT BIG DATA? AN ENERGY EFFICIENT BIG DATA GATHERING ALGORIT...

More from Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...

Nexgen Technology

Nexgen Technology Address: Nexgen Technology No :66,4th cross,Venkata nagar, Near SBI ATM, Puducherry. Email Id: mailtonexgentech@gmail.com. www.nexgenproject.com Mobile: 9791938249,9025656779 NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CH...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHENN...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHE...

Nexgen Technology

MECHANICAL PROJECTS IN PONDICHERRY, 2020-21 MECHANICAL PROJECTS IN CHENNA...

Nexgen Technology

Ieee 2020 21 vlsi projects in pondicherry,ieee vlsi projects in chennai

Nexgen Technology

Ieee 2020 21 power electronics in pondicherry,Ieee 2020 21 power electronics

Nexgen Technology

Ieee 2020 -21 ns2 in pondicherry, Ieee 2020 -21 ns2 projects,best project cen...

Nexgen Technology

Ieee 2020 21 ns2 in pondicherry,best project center in pondicherry,final year...

Nexgen Technology

Ieee 2020 21 java dotnet in pondicherry,final year projects in pondicherry,pr...

Nexgen Technology

Ieee 2020 21 iot in pondicherry,final year projects in pondicherry,project ce...

Nexgen Technology

Ieee 2020 21 blockchain in pondicherry,final year projects in pondicherry,bes...

Nexgen Technology

Ieee 2020 -21 bigdata in pondicherry,project center in pondicherry,best proje...

Nexgen Technology

Ieee 2020 21 embedded in pondicherry,final year projects in pondicherry,best...

Nexgen Technology

This document lists over 100 potential final year project titles related to embedded systems and Internet of Things (IoT) technologies across various application domains, such as medical and healthcare, smart homes, agriculture, transportation, and more. It provides contact information for Praveen Kumar to discuss project details. The titles are organized under categories like embedded handheld products, cyber physical systems, pervasive computing, energy management, bioscience, and others.

More from Nexgen Technology (20)