This document provides an overview of social network analysis concepts including:
1. Key terms like actors, ties, relations, dyads, triads, ego networks, sociograms, and centrality measures.
2. Common network models and properties including small world networks, preferential attachment, degree distributions, and assortativity.
3. Common network analysis tasks such as link prediction, diffusion modeling, clustering, and structural analysis techniques like motif detection and blockmodeling.
Keynote given at the workshop for Artificial Intelligence meets the Web of Data on Pragmatic Semantics.
In this keynote I argue that the Web of Data is a Complex System or Marketplace of Ideas rather than a classical Database, and that the model theory on which classical semantics are based is not appropriate in all situations, and propose an alternative "Pragmatic Semantics" based on optimisation of possible interpretations. .
In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication
intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental
differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between
users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for
link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate microblogging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.
Keynote given at the workshop for Artificial Intelligence meets the Web of Data on Pragmatic Semantics.
In this keynote I argue that the Web of Data is a Complex System or Marketplace of Ideas rather than a classical Database, and that the model theory on which classical semantics are based is not appropriate in all situations, and propose an alternative "Pragmatic Semantics" based on optimisation of possible interpretations. .
In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication
intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental
differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between
users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for
link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate microblogging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.
EgoSystem: Presentation to LITA, American Library Association, Nov 8 2014James Powell
The Internet represents the connections among computers and devices, the world wide web is a network of interconnected documents, and the semantic web is the closest thing we have today to a network of interconnected facts. Noticeably absent from these global networks is any sort of open, formal representation for an online global social network. Each users' online presence, and its immediate social network, are isolated and typically only available within the confines of the social networking site that hosts it. Discovery across explicit online social networks and implicit social networks such as those that can be inferred from co-authorship relationships and affiliations is, for all practical purposes, impossible. And yet there are practical and non-nefarious reasons why an organization might be interested in exploring portions of such a network. Outreach is one such interest. Los Alamos National Laboratory (LANL) prototyped EgoSystem to harvest and explore the professional social networks of post doctoral students. The project's goal is to enlist past students and other Lab alumni as ambassadors and advocates for LANL's ongoing mission. During this talk we will discuss the various technologies that support the EgoSystem and demonstrate some of its capabilities.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
Natural Language Processing in R (rNLP)fridolin.wild
The introductory slides of a workshop given to the doctoral school at the Institute of Business Informatics of the Goethe University Frankfurt. The tutorials are available on http://crunch.kmi.open.ac.uk/w/index.php/Tutorials
- What is Clustering, Honeypots and Density Based Clustering?
- What is Optics Clustering and how is it different than DB Clustering? …and how
can it be used for outlier detection.
- What is so-called soft clustering and how is it different than clustering? …and how
can it be used for outlier detection.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Multi-Model Data Query Languages and Processing ParadigmsJiaheng Lu
Specifying users' interests with a formal query language is a typically challenging task, which becomes even harder in the context of multi-model data management because we have to deal with data variety. It usually lacks a unified schema to help the users issuing their queries, or has an incomplete schema as data come from disparate sources. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating and querying the multi-model data in a single system. This tutorial aims to offer a comprehensive presentation of a wide range of query languages for MMDBs and to make comparisons of their properties from multiple perspectives. We will discuss the essence of cross-model query processing and provide insights on the research challenges and directions for future work. The tutorial will also offer the participants hands-on experience in applying MMDBs to issue multi-model data queries.
EgoSystem: Presentation to LITA, American Library Association, Nov 8 2014James Powell
The Internet represents the connections among computers and devices, the world wide web is a network of interconnected documents, and the semantic web is the closest thing we have today to a network of interconnected facts. Noticeably absent from these global networks is any sort of open, formal representation for an online global social network. Each users' online presence, and its immediate social network, are isolated and typically only available within the confines of the social networking site that hosts it. Discovery across explicit online social networks and implicit social networks such as those that can be inferred from co-authorship relationships and affiliations is, for all practical purposes, impossible. And yet there are practical and non-nefarious reasons why an organization might be interested in exploring portions of such a network. Outreach is one such interest. Los Alamos National Laboratory (LANL) prototyped EgoSystem to harvest and explore the professional social networks of post doctoral students. The project's goal is to enlist past students and other Lab alumni as ambassadors and advocates for LANL's ongoing mission. During this talk we will discuss the various technologies that support the EgoSystem and demonstrate some of its capabilities.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
Natural Language Processing in R (rNLP)fridolin.wild
The introductory slides of a workshop given to the doctoral school at the Institute of Business Informatics of the Goethe University Frankfurt. The tutorials are available on http://crunch.kmi.open.ac.uk/w/index.php/Tutorials
- What is Clustering, Honeypots and Density Based Clustering?
- What is Optics Clustering and how is it different than DB Clustering? …and how
can it be used for outlier detection.
- What is so-called soft clustering and how is it different than clustering? …and how
can it be used for outlier detection.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Multi-Model Data Query Languages and Processing ParadigmsJiaheng Lu
Specifying users' interests with a formal query language is a typically challenging task, which becomes even harder in the context of multi-model data management because we have to deal with data variety. It usually lacks a unified schema to help the users issuing their queries, or has an incomplete schema as data come from disparate sources. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating and querying the multi-model data in a single system. This tutorial aims to offer a comprehensive presentation of a wide range of query languages for MMDBs and to make comparisons of their properties from multiple perspectives. We will discuss the essence of cross-model query processing and provide insights on the research challenges and directions for future work. The tutorial will also offer the participants hands-on experience in applying MMDBs to issue multi-model data queries.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
13. Describing Networks
• Geodesic
– shortest_path(n,m)
• Diameter
– max(geodesic(n,m)) n,m actors in graph
• Density
– Number of existing edges / All possible edges
• Degree distribution
14. Types of Networks/Models
• A few quick examples
– Erdős–Rényi
• G(n,M): randomly draw M edges between n nodes
• Does not really model the real world
– Average connectivity on nodes conserved
15. Types of Networks/Models
• A few quick examples
– Erdős–Rényi
– Small World
• Watts-Strogatz
• Kleinberg lattice model
16. NE
MA
Milgram’s experiment (1960’s):
Given a target individual and a particular property, pass the message to a person
you correspond with who is “closest” to the target.
Small world experiments then
17. Watts-Strogatz Ring Lattice Rewiring
• As in many network generating algorithms
• Disallow self-edges
• Disallow multiple edges
Select a fraction p of
edges
Reposition on of their
endpoints
Add a fraction p of
additional edges leaving
underlying lattice intact
19. Kleinberg Lattice Model
nodes are placed on a lattice and
connect to nearest neighbors
additional links placed with puv ~ r
uv
d
Kleinberg, ‘The Small World Phenomenon, An Algorithmic Perspective’
(Nature 2000)
20.
21. A little more on degree distribution
• Power-laws, zipf, etc.
Distribution of users among
web sites
CDF of users to sites
Sites ranked by popularity
22. A little more on degree distribution
• Pareto/Power-law
– Pareto: CDF P[X > x] ~ x-k
– Power-law: PDF P[X = x] ~ x-(k+1) = x-a
– Some recent debate (Aaron Clauset)
• http://arxiv.org/abs/0706.1062
• Zipf
– Frequency versus rank y ~ r-b (small b)
• More info:
– Zipf, Power-laws, and Pareto – a ranking tutorial
(http://www.hpl.hp.com/research/idl/papers/ranking
/ranking.html)
23. Types of Networks/Models
• A few quick examples
– Erdős–Rényi
– Small World
• Watts-Strogatz
• Kleinberg lattice model
– Preferential Attachment
• Generally attributed to Barabási & Albert
24. Basic BA-model
• Very simple algorithm to implement
– start with an initial set of m0 fully connected nodes
• e.g. m0 = 3
– now add new vertices one by one, each one with exactly m
edges
– each new edge connects to an existing vertex in proportion
to the number of edges that vertex already has →
preferential attachment
25. Properties of the BA graph
• The distribution is scale free with exponent a = 3
P(k) = 2 m2/k3
• The graph is connected
– Every new vertex is born with a link or several links
(depending on whether m = 1 or m > 1)
– It then connects to an ‘older’ vertex, which itself
connected to another vertex when it was introduced
– And we started from a connected core
• The older are richer
– Nodes accumulate links as time goes on, which gives older
nodes an advantage since newer nodes are going to attach
preferentially – and older nodes have a higher degree to
tempt them with than some new kid on the block
33. Centrality Measures
• Degree centrality
– Edges per node (the more, the more important the node)
• Closeness centrality
– How close the node is to every other node
• Betweenness centrality
– How many shortest paths go through the edge node
(communication metaphor)
• Information centrality
– All paths to other nodes weighted by path length
• Bibliometric + Internet style
– PageRank
34. Tie Strength
• Strength of Weak Ties (Granovetter)
– Granovetter: How often did you see the contact that helped you find the job prior
to the job search
• 16.7 % often (at least once a week)
• 55.6% occasionally (more than once a year but less than twice a week)
• 27.8% rarely – once a year or less
– Weak ties will tend to have different information than we and our close contacts
do
weak ties will tend to have high
beweenness and low transitivity
37. Link Prediction in Social Net Data
• We know things about structure
– Homophily = like likes like or bird of a feather flock
together or similar people group together
– Mutuality
– Triad closure
• Various measures that try to use this
38. Link Prediction
• Simple metrics
– Only take into
account graph
properties
Liben-Nowell, Kleinberg (CIKM’03)
( ) ( )
1
log | ( ) |
z x y z
Γ(x) = neighbors of x
Originally: 1 / log(frequency(z))
39. Link Prediction
• Simple metrics
– Only take into
account graph
properties
Liben-Nowell, Kleinberg (CIKM’03)
,
1
| |
l l
x y
l
paths
Paths of length l (generally 1)
from x to y
weighted variant is the number of
times the two collaborated
40. Link Prediction in Relational Data
• We know things about structure
– Homophily = like likes like or bird of a feather flock
together or similar people group together
– Mutuality
– Triad closure
• Slightly more interesting problem if we have
relational data on actors and ties
– Move beyond structure
41. Relationship & Link Prediction
advisorOf?
Employee /contractor
Salary
Time at company
…
42. Link/Label Prediction in Relational Data
• Koller and co.
– Relational Bayesian Networks
– Relational Markov Networks
• Structure (subgraph templates/cliques)
– Similar context
– Transitivity
• Getoor and co.
– Relationship Identification for Social Network Discovery
• Diehl/Namata/Getoor AAAI’07
– Enron data
• Traffic statistics and content to find supervisory relationships?
– Traffic/Text based
– Not really identification, more like ranking
44. Epidemiological
• Viruses
– Biological, computational
– STDs, needle sharing, etc.
– Mark Handcock at UW
• Blog networks
– Applying SIR models (Info Diffusion Through Blogspace, Gruhl et
al.)
• Induce transmission graph, cascade models, simulation
– Link prediction (Tracking Information Epidemics in Blogspace,
Adar et al.)
• Find repeated “likely” infections
– Outbreak detection (Cost-effective Outbreak Detection in
Networks, Leskovec et al.)
• Submodularity
49. Blockmodels
• Actors are portioned into positions
– Rearrange rows/columns
• The sociomatrix is then reduced to a smaller
image
• Hierarchical clustering
– Various distance metrics
• Euclidean, CONvergence of CORrelation (CONCOR)
• Various “fit” metrics
56. Network motif detection
• How many more motifs of a certain type exist
over a random network
• Started in biological networks
– http://www.weizmann.ac.il/mcb/UriAlon/
57. Basic idea
• construct many random graphs with the same
number of nodes and edges (same node
degree distribution?)
• count the number of motifs in those graphs
• calculate the Z score: the probability that the
given number of motifs in the real world
network could have occurred by chance
58.
59. Generating random graphs
• Many models don’t preserve the desired
features
• Have to be careful how we generate
62. Privacy
• Emerging interest in anonymizing networks
– Lars Backstrom (WWW’07) demonstrated one of
the first attacks
• How to remove labels while preserving graph
properties?
– While ensuring that labels cannot be reapplied
65. Books/Journals/Conferences
• Social Networks/Phs. Rev
• Social Network Analysis (Wasserman + Faust)
• The Development of Social Network Analysis
(Freeman)
• Linked (Barabsi)
• Six Degrees (Watts)
• Sunbelt/ICWSM/KDD/CIKM/NIPS
67. Assortativity
• Social networks are assortative:
– the gregarious people associate with other gregarious people
– the loners associate with other loners
• The Internet is disassorative:
Assortative:
hubs connect to hubs
Random
Disassortative:
hubs are in the periphery