Paramita Dey

A Comparative Analysis of Different Social Network Parameters Derived from Facebook Profiles

In social network analysis (SNA), using online social media, it is possible to collect large open source information and to analyze those data for knowing the characteristics of these networks. The main objective of this work is to study online social network parameters commonly used to explain social structures. In this paper, we have extracted data from the three real-time facebook accounts using Netvizz application. Gephi, a open source free software, is used for analysis and evaluation of these network parameters. This analysis shows some well-known network parameters like calculating clustering coefficient (CC) of clusters, group formation, finding node degree distribution (NDD), identifying influential node etc., which can be used for further feature extraction.

Ego Based Community Detection in Online Social Network

Social network can be represented by a graph, where individual users are represented as nodes/vertices and connections between them are represented as edges of the graph. The classification of people based on their tastes, choices, likes or dislikes are associated with each other, forms a virtual cluster or community. The basis of a better community detection algorithm refers to within the community the interaction will be maximized and with other community the interaction will be minimized. In this paper, we are proposing an ego based community detection algorithm and compared with three most popular hierarchical community detection algorithms, namely edge betweenness, label propagation and walktrap and compare them in terms of modularity, transitivity, average path length and time complexity. A network is formed based on the data collected from a Twitter account, using Node-XL and I-graph and data are processed in R based Hadoop framework.

The importance of seed nodes in spreading information in social networks: A case study

In todays world, social media is a powerful tool: spreading information, and changing the way we receive news. It often reaches faster and farther than any any other channel. The availability of large scale data on online social media motivates our questions on social behavior in spreading information on the network. We conduct extensive experiments on Twitter data, to determine how important some people are in spreading information to the world. The Twitter data used in this study is stored on an HDFS and manipulated using algorithms framed in the MapReduce paradigm of Hadoop. An information flow network is generated from the Twitter data (81540798 tweets) based on the hashtags #Brexit, #Euro and #Rio. We study the effectiveness of K-core and PageRank algorithms in identifying important seeds in social networks, by comparing their outputs against the true seeds obtained from the two-phase algorithm. K-core performs better by most similarity indices, when compared to PageRank.

Social Network Analysis of Different Parameters Derived from Realtime Profile

The main objective of this work is to study network parameters commonly used to explain social structures. In this paper, we extract data from a real-time Facebook account using Netvizz application, analyze and evaluate network parameters on some widely recognized graph topology using GEPHI software.

Social Network Analysis of Cricket Community Using a Composite Distributed Framework: From Implementation Viewpoint

This paper proposes an alternate ranking system based on social network metrics and their evaluation in a composite distributed framework. The most important aspect of social network domain is voluminous data. In order to know key trends, predictive analysis of huge sets of raw data can be effective. Again these analytics are generally very compute-intensive. The most efficient solution of such compute-intensive analysis is to map such problems in a distributed domain. The main contributions of this paper are twofold. First, the analysis of cricket community from the viewpoint of social network and finding ranking of players and countries based on the properties of graph centrality measures. Second, the paper proposes a comprehensive distributed framework to offer infrastructural support for the large data analysis as well as graph processing. Hadoop is an open-source framework to store and process large data sets in a cloud environment. MapReduce is a popular programming model for processing that data in a distributed manner. However, MapReduce alone is not efficient for graph processing. Giraph is an alternative programming paradigm for graph processing in Hadoop. Using a practical case study of social network analysis of cricket community, this paper captures the significance of the alternate ranking in this sport, as well as shows effectiveness of the proposed framework in the process of analyzing voluminous data.

Information spreading in Online Social Networks: A case study on Twitter network

Information propagation in online social media draw attention in different research domains due to its influence and importance in public domain. The complexity of this research problem arises due to its huge volume and transient nature. For different user domain data processing, methodologies and derivations of information spreading are quite different in nature. In this paper, we study information propagation in online social network twitter, considering different centrality measures and simulate through random walk. Assuming that the structure of these websites is a kind of scale free network but exhibits the property of small world network, we compare information propagation to sight the most pivotal nodes in the network in aspect of information propagation.

Small World Network Formation and Characterization of Sports Network

The motivation of this paper is formation of sports network and characterization of the small world network phenomenon by analyzing the data of individual players of a team. Analysis of the network suggests that sports network can be considered as small world and inherits all characteristics of small world network. Making a quantitative measure for an individual performance in the team sports is important in respect to the fact that for team selection of International football matches, from a pool of best players, only 11 players can be selected for the team. The statistical record of each player is considered as a traditional way of quantifying the performance of a player. But other criteria like performing against a strong opponent or executing a brilliant performance against a strong team deserves more credit. In this paper, a method based on social networking is presented to quantify the quality of player’s efficiency and is defined as the total matches played between each team members of individual teams and the members of different teams. The application of Social Network Analysis (SNA) is explored to measure performances and rank of the players. A bidirectional weighted network of players is generated using the information collected from English Premier League (2014–2015) and used for network formation.