This document discusses applying computational analytics and social network analysis to SPOD data. It introduces concepts like centrality metrics, community detection algorithms, and dynamic graph analysis to study networks over time. Examples of social networks like friendships and email are provided. The document also discusses using the open-source Gephi tool to visualize networks and explore metrics. Overall, it explores how network analysis can provide insights from SPOD data to support tasks like identifying skilled users, encouraging participation, and understanding relationships between entities in the data.

1. Computational Analytics in SPOD
Gennaro Cordasco, Carmine Spagnuolo, Vittorio Scarano

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Overview
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Introduction
Some example
Some Metrics and Methods
Social and Data Network Analysis (SDNA) in SPOD
Discussion
Gephi: The Open Graph Viz Platform
Network over time

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Introduction
Social
Network
Analysis

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The power of social networks

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What is a social network?

6. • Social network analysis and data mining:
– Detection of criminal activity, counter-terrorism, homeland security,
and intelligence
– Analysis of relationships within companies
– Sociological and anthropological studies
– Reciprocal trust schemes such as eBay ratings
– Recommended friends on Facebook
– Filter or recommend social media content
• – …
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Applications of social networks

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Some
Examples

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Friendship network

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Email network
Nodes = People
Links = Emails
Source: orgnet.com

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Karate Club

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Romantic and Sexual Relations

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Football: Spain won 2010’s World Cup

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What can we learn by using these models?
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• Can we mine SPOD data in order to:
• support other analysis (like Dialogue
Games)?
• encourage users participation?
• identify skilled and/or reliable users?
• …
• Idea: assuming that we are able to
model the data according to some
relationships between different
entities:
• we can exploit Network analysis
tools.
Network analysis in SPOD

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Metrics
and
Methods

16. • Assumption: important actors are
involved with others extensively.
• The problem is find out
automatically which are the central
actors.
• Metrics:
• Closeness Centrality
• Betweenness Centrality
• Eigenvalue Centrality
• Prestige
• PageRank Algorithm
• Hits Algorithm
• …
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Centrality

17. • Methods:
o community detection is key to
understanding the structure of
networks.
• In SPOD it can be used to
• identify users with common interest (and
suggest friendship);
• identify related data;
• suggest the proper way of visualizing data.
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Community detection

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Network
over
the
Time

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Networks over time
• Does the leader of a discussion change over time?
• Is it true that participate actively in a discussion enhances users’ ability?
• Dynamic graph analysis (DGA) is a novel approach for Network analysis.
• DGA analyses how a network and its metrics evolve over time.

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in
Social
SPOD
and
Data
Analysis
Network

21. • It is easy to analyse a snapshot of a Network at time t (Gephi does it for you).
• The key point is to identify the Network structure and semantics
• Different goals require dedicated approaches and are based on different networks.
• For instance, if we are going to study interaction among users, we need a network
where:
• the users are the entities;
• the relations are "who communicate with whom”.
• Even in this simple case, we should also define some timing intervals in order to
evaluate whether the interactions change along the time.
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Social and Data Network Analysis (SDNA) in SPOD

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Social and Data Network Analysis (SDNA) in SPOD
• Another approach could be
define a network that map
users and data as a Users-
Datalets Network.

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Gephi
and
visualization
exploration

24. • Gephi is an open-source
network analysis and
visualization software
package.
• Gephi offers the most
common metrics for
network analysis:
• Betweenness Centrality;
• Closeness;
• Diameter;
• Clustering Coefficient;
• PageRank;
• Community detection;
• …
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Gephi: The Open Graph Viz Platform

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Discussion

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Discussion
• Goals
• Networks
• Ideas