Social Media Mining - Chapter 8 (Influence and Homophily)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 7 (Information Diffusion)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 8 (Influence and Homophily)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 7 (Information Diffusion)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Social Media Mining - Chapter 10 (Behavior Analytics)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Clustering Methods and Community Detection with NetworkX. A slide deck for the NTU Complexity Science Winter School.
For the accompanying iPython Notebook, visit: http://github.com/eflegara/NetStruc
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Community detection from research papers (AAN dataset) using the algorithms:
K-Means
Louvain
Newman-Girvan
github link to code: https://goo.gl/CXej44
github link to project web page: http://goo.gl/7OOkhI
youtube link to video:https://goo.gl/SCpamf
dropbox link to ppt report video: https://goo.gl/cgACzU
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
Network centrality measures and their effectivenessemapesce
Often centrality measures are used in social network analysis. The goal of this presentation is to explain how different centrality works and how they can be compared.
Centrality measures covered: degree, closeness, harmonic, Lin's index, betweenness, eigenvector, seeley's index, pagerank, hits, SALSA
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Social Media Mining - Chapter 10 (Behavior Analytics)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Clustering Methods and Community Detection with NetworkX. A slide deck for the NTU Complexity Science Winter School.
For the accompanying iPython Notebook, visit: http://github.com/eflegara/NetStruc
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Community detection from research papers (AAN dataset) using the algorithms:
K-Means
Louvain
Newman-Girvan
github link to code: https://goo.gl/CXej44
github link to project web page: http://goo.gl/7OOkhI
youtube link to video:https://goo.gl/SCpamf
dropbox link to ppt report video: https://goo.gl/cgACzU
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
Network centrality measures and their effectivenessemapesce
Often centrality measures are used in social network analysis. The goal of this presentation is to explain how different centrality works and how they can be compared.
Centrality measures covered: degree, closeness, harmonic, Lin's index, betweenness, eigenvector, seeley's index, pagerank, hits, SALSA
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
Applications of community detection in bibliometric network analysisNees Jan van Eck
In this talk, we focus on the analysis of bibliometric networks, and in particular on the detection of communities in these networks. We start by demonstrating VOSviewer, a popular software tool for visualizing bibliometric networks. We discuss the techniques used by VOSviewer for visualizing bibliometric networks and for detecting communities in these networks. We pay special attention to the close relationship between visualization and community detection, and we discuss the unified approach to visualization and community detection that is implemented in VOSviewer. We then shift our attention to community detection in very large citation networks, including millions of publications and hundreds of millions of citation relations. We show how community detection techniques can be used to construct highly detailed classification systems of science. We also discuss applications of such classification systems to science policy questions. Finally, we demonstrate CitNetExplorer, a new software tool in which community detection techniques are used to support the large-scale analysis of citation networks. We use CitNetExplorer to analyze the citation network of publications on network science and in particular on community detection.
Big data matrix factorizations and Overlapping community detection in graphsDavid Gleich
In a talk at the Chinese Academic of Sciences Institute for Automation, I discuss some of the MapReduce and community detection methods I've worked on.
A network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping)a sets of nodes such that each set of nodes is densely connected internally.
The method of identifying similar groups of data in a data set is called clustering. Entities in each group are comparatively more similar to entities of that group than those of the other groups.
Action and content based Community Detection in Social Networksritesh_11
With the exponential growth and availability of data, the techniques for analyzing it are also
maturing. An emerging trend is to model entities and their interactions as a Social Network.
Extracting communities from these networks provides useful insights into the latent structure of
the underlying network as well as the dynamics of how the network evolves.
The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is
community structure, or clustering i.e. the organization of vertices in clusters, with many edges
joining vertices of same clusters and few edges joining vertices of different clusters. Such
clusters can be considered as independent groups of graphs playing a similar role.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
3. Member-Based Community Detection
1-Similarity characteristics are more often in the same community
Important Node Feature :
node similarity - node degree(familiarity) - node reachability
similarity is based on overlap between the neighborhood
Two Methods to find similarity:
5. Member-Based Community Detection
2- sub graphs based on node degrees is a clique
We can cut graph to complete sub graphs -> NP hard
use brute force-polynomial solvable - use cliques as core of community
Brute-force clique identification Method -> can find all maximal cliques in a
graph
Clique percolation method -> CMP
6.
7.
8. Though sharing no neighborhood overlap, the social circles of these players
(coach, players, fans, etc.) might look quite similar due to their social status. In
other words, nodes are regularly equivalent when they are connected to nodes
that are themselves similar (a self-referential definition).
9. Member-Based Community Detection
3-The two extremes of reachability
(1) there is a path between them (regardless of the distance)
BFS & DFS Methods ->is not useful in large community
(2) so close to be immediate neighbors
we can find shortest paths between their nodes in Clique
but There are predefined sub graphs, with roots in community
10.
11. Group-Based Community Detection
In graph-based clustering, we cut the graph into several partitions
Cut size = how many cut edge and the summation of weights
1
2 4
Minimum Cut
Are not perfect coz
often find
singleton nods
Balance Cut
More Balance Cut
12. Group-Based Community Detection
1-balance partitioning mod :
Graph G = (V,E) (Vertices, Edge) to K partition that have Pi vertices
P = (P1, P2, P3, ....... , Pk) , Pi ∩ Pj = 0 , 𝑖=1
𝑘
Pi=V , ¯Pi=V-Pi
13. Group-Based Community Detection
1-balance partitioning mod in matrix format :
Let matrix X Xi,j= 1 if node i is in community j , otherwise Xi,j= 0
Let D = diag(d1, d2, …. ,dn)
X’AX -> edge inside i community
15. Group-Based Community Detection
Robust Communities:
goal is to find sub graphs robust enough such that removing edges or nodes
does not disconnect the sub graph
K-vertex connected graph method -> we must find minimum number of
nodes that must be removed to disconnect the graph =K
minimum degree for any node in the graph should not be less than k
16. Group-Based Community Detection
Modular Communities:
How community structure found is at random(structures must far from random)
G(V, E) , |E| = m , we have degrees but don’t have Edges , v
Consider vi , vj nodes with di , dj degrees P(connect vi to vj ) =
di
𝑖 di
=
di
2𝑚
SO number of edges between vi and vj ->
di ∗ dj
2𝑚
17. Group-Based Community Detection
Modular Communities:
modularity maximization try to maximize this distance
Consider Graph G = (V,E) (Vertices, Edge)
to K partition that have Pi vertices P = (P1, P2, P3, ....... , Pk)
For partition Px this distance can be defined
generalize by partitioning P with k partitions
19. Group-Based Community Detection
Dense Communities:
Cliques , clubs, and clans are examples of connected dense
we focus on sub graphs that should be disconnected
We can utilize the brute-force clique identification algorithm
Density
20. Group-Based Community Detection
Hierarchical Communities:
community can have sub/super communities. Girvan-Newman algorithm
designed for divisive hierarchical clustering
Girvan-Newman have measure called “edge between ness” removes edges
with higher edge between ness.
For an edge E, edge between ness is defined as the number Edge of shortest
paths between node pairs (Vi , Vj) such that the shortest path Between ness
between Vi and Vj passes through E.
21. Group-Based Community Detection
Hierarchical Communities (Girvan-Newman Algorithm):
1. Calculate edge between ness for all edges in the graph.
2. Remove the edge with the highest between ness
3. Recalculate between ness for all edges a edged by the edge removal
4. Repeat until all edges are removed