A Comprehensive Analysis of Social Network Mining

Computational Social Networks, 2012

Social network research relies on a variety of data sources, depending on the problem scenario and the questions, which the research is trying to answer or inform. Social networks are very popular nowadays and the understanding of their inner structure seems to be promising area. Cluster analysis has also been an increasingly interesting topic in the area of computational intelligence and found suitable in social network analysis in its social network structure. In this chapter, we use k-cluster analysis with various performance measures to analyse some of the data sources obtained for social network analysis. Our proposed approach is intended to address the users of social network, that will not only help an organization to understand their external and internal associations but also highly necessary for the enhancement of collaboration, innovation and dissemination of knowledge.

Pattern Recognition Letters, 2011

In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.

International Journal of Computer Applications, 2014

Complex networks are real graphs (networks) with non-trivial topological features. The empirical study of real-world networks like computer networks and social networks gives insights into the structures and properties of such networks. Identification of community structure is one of the important problems in social networks. Tightly knit group of nodes (Cluster) characterized by a relatively high density of ties (links) tend to be greater than the nodes that have average probability of ties randomly established [8][16]. In this paper a novel clustering algorithm is developed in complex social networks to detect the communities with close relations where in, everybody is aware of every other in their group called cluster. Determining such groups is the main concern of this paper. Some of the social networks are online Facebook, LinkedIn, Twitter and day today socializing. Graph Theoretic approach is followed for finding the clusters. Perfect graph structures are investigated in the complex social networks.

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules . Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the 1 including the tools employed as well as names of their authors.

International Journal of Computer Applications

In the recent decade, the Online Social Network (OSN) has gained remarkable attention. Accessing to OSN sites such as Twitter, Facebook, LinkedIn and Google Plus; the most dominant social media in the world, through the internet and the web 2.0 technologies has become more comfortable. These days through these online social networks, it becomes very easy for anyone to meet the people of the same interests for learning and sharing precious information. Online Social Network Analysis (OSNA) is an essential and important technique to understand the social structure, social relationships and social behaviors of OSN. OSNA deals with the interaction between individuals by considering them as nodes of a network whereas their relations are mapped as network edges. Now, it has increased various challenges for the evolution of the web and simultaneously increased the dynamic changes in its structure so it became harder to manually analyze very broad OSN. This survey investigates the current progression in the field of knowledge discovery in OSNA and covers all basic techniques of Data, Text, and Web mining that are widely used for the exploration of the unstructured and structured data available on the OSNA. The targets for OSNA are mainly focused on resources from the web, such as content, structure, and user behaviors. The main goal of this paper is to introduce a roadmap for the researchers who are interesting on the topics of knowledge discovery techniques for discovering totally different trends in OSN data. Discussion of all the challenges that face researchers in OSNA is also included.

2016

The rapid generation and uncontrollable accumulation of the social network data has raised a real issue now, because the data are vast, noisy, distributed, unstructured and dynamic. Since this data can be mined by using web mining techniques, social network analysis and link prediction algorithms, in this article we try to understand the social structure and issues surrounding mining social network data. We will also be looking at the link prediction problems in dynamic social networks and the important techniques that can be applied as an attempt for a resolution.

2021

Social Network Analysis (SNA) is computing and mapping of relationships and movements between people, groups, organizations, URLs, computers and other connected information/knowledge bodies. The people and groups are represented as nodes whereas the show movements or relationships between the nodes are represented as links. Community discovery is one of the challenging problems in any networks, we have proposed a community discovery algorithm which is producing better result than existing algorithm. Proposed and Existing algorithms are based on edge betweenness [1] score. We show that novel approach algorithm performs quite well on bench mark data sets and a few real-world networks. We have also considered the core nodes of the graph and identifies the importance for each node. IndexTerms Edge betweenness, Betweenness centrality, GN algorithm, Novel approach

2013

In this paper we take into consideration the concepts of using algorithmic and data mining perspective of Online Social Networks (OSNs), with special emphasis on latest hot topics of research area. There are several factors which has made the study of OSNs gain enormous importance by researchers. Few such factors include the availability of huge amount of OSN data, the representation of OSN data as graphs, and so on. Analysis of data in OSNs also has a great prospective for researchers in a variety of disciplines. Hence this paper gives an idea about the key topics of using data mining in OSNs which will help the researchers to solve those challenges that still exist in mining OSNs.

2012

Social network research relies on a variety of data sources, depending on the problem scenario and the questions, which the research is trying to answer or inform. Social networks are very popular nowadays and the understanding of their inner structure seems to be promising area. Cluster analysis has also been an increasingly interesting topic in the area of computational intelligence and found suitable in social network analysis in its social network structure. In this chapter, we use k-cluster analysis with various performance measures to analyse some of the data sources obtained for social network analysis. Our proposed approach is intended to address the users of social network, that will not only help an organization to understand their external and internal associations but also highly necessary for the enhancement of collaboration, innovation and dissemination of knowledge. M. Panda ( ) Department of ECE, Gandhi Institute for Technological Advancement (GITA), Bhubaneswar, Odi...

2015

A social network is a social structure of people, related (directly or indirectly) to each other through a common relation or interest. Social network analysis (SNA) is the study of social networks to understand their structure and behavior. For studying structural and behavioral properties of these networks, communities are identified by grouping of individuals according to given context into subgroups. Community detection is very rich domain in social network analysis as it is useful in various domains like business, marketing, healthcare etc. Data analytic techniques such as data mining and predictive modeling are being used to gain new insights into social network analysis (SNA). This has the unique ability to play a new role in exploring the context and situations that lead to efficient and effective predictions. Identifying these social communities can bring benefit to understanding and predicting user’s behaviors. This paper is an attempt to study the various approaches for c...