* Community Detection with Edge Content in Social Media Networks* Community Detection in Social Media by Leveraging Interactions and Intensities

Presented By: Mojtaba Rezaei & Reza Habibi Kerahroudi

University Of TehranNetworked Systems Engineering

Community Detection in Social MediaGraph Algorithms Course Wikipedia

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Community Detection with Edge Content in Social Media Networks

Presented By: Mojtaba Rezaei

University Of TehranNetworked Systems Engineering

Introduction

Most community detection algorithms use

the links between the nodes in order to

determine the dense regions in the graph.

in many recent applications, edge content is available in order to provide better supervision to the community detection process

Introduction (cont.)

An important problem in the area of social media

is that of community detection.

In the problem of community detection, the goal is to partition the network into dense regions of the graph.

Introduction (cont.)

a lot of rich information is encoded in the

content of

the interactions among the actors in the

network.

E-mail networkswe will see that edge content provides a number of unique distinguishing characteristics of the communities which cannot be modeled by node content.

Illustration of a social media network

The nodes represent users while the edges represent the favored images shared by the users

Introduction (cont.)

Edge-based content is much more

challenging, because the different interests of

the same actor node may be reflected in

different edges.

We will show that such an approach provides unique insights which are not possible with the useof pure link-based or content-based methods.

Community Detection With Edge Content

most community detection methods are

focused on partitioning the nodes based on

linkage, and we are interested in partitioning

the edges based on both linkage and content.

Community Detection With Edge Content (cont.)

when there are no links, the problem

defaults to the pure content-based

clustering problem.

Data Sets Enron Email Data Set

200, 399 messages belonging to 158 members of senior management

Flickr Social Network Data Set 15 popular Flickr user groups, including “family”, “auto”, “concerts”, “pet portraits”, “kids and

nature”, “street”, “art”,“wide party,” “folk music“ , "magic city”, “party favors”, "British

politics”, “youth basketball”, “fast food", "fancy dress party” and “great sky.”

This social media network has 4, 703 users in 15 groups

Community Detection in Social Media by Leveraging Interactions and Intensities

Presented By: Reza Habibi Kerahroudi

University Of TehranNetworked Systems Engineering

Introduction

User interaction networks capture users’ associations

derived from their activities in social media such as:

commenting on others’ posts, replying to comments,

referencing other users, etc.

Communities can be generally defined as groups of users

that are "closely-knit”, in the sense that a group’s

interconnections are more dense compared to connections

with the rest of the network.

Introduction (cont.)

Our focus is on revealing the types of communities generated with respect to certain events by analyzing them in the dimensions of size, topic diversity and time span.

VERTEX STRUCTURE STRUCTURAL SIMILARITY

ε – NEIGHBORHOOD

CORE VERTEX DIRECT STRUCTURE REACHABILITY STRUCTURE REACHABILITY STRUCTURE CONNECTIVITY STRUCTURE-CONNECTED CLUSTER CLUSTERING HUB OUTLIER

SCAN algorithm

Getting from SCAN to WSCAN SCAN discovers cohesive network subclusters

based on parameters μ and , which control the minimum community’s size and the minimum structural similarity between two community’s nodes, respectively.

To adapt SCAN for weighted interaction networks we propose weighted structure reachability for

(μ, )-cores’ detection.

Real-World Networks

For experimentation we have generated a

network based on Twitter user interactions,

(i.e. mentions, replies, retweets), extracted

from data collected via the Twitter Streaming

API with topic-related keywords.

Our selected topic refers to the official Euro group meetings (of Euro zone's finance ministers)

Real-World Networks Our EUROGROUP dataset

(covering 8 meetings from 13/06/12 to 30/11/12) acts as an exemplary case study of a series of events held at different time instances, having the same participants with a common generic context (i.e. the Euro zone's monetary issues), but different focus (depending on the agenda). The dataset spans 227 days and comprises: 29529 tweets, 10305 interactions and 3015 different users.

EUROGROUP meetings, tweets, and communities

Classification of the most significant topics based on interest intensity and diffusion