Fast Detection of Community Structures using Graph Traversal in Social Networks

Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real-time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in O(|V | + |E|) time to create an initial cover before using modularity maximization to get the final cover. Keywords - community detection; Influenced Neighbor Score; brokers; community nodes; communitiesComment: 29 pages, 9 tables, and 13 figures. Accepted in "Knowledge and Information Systems", 201

A NOVEL CLUSTER BASED WORMHOLE AVOIDANCE ALGORITHM FOR MOBILE AD- HOC NETWORKS

A severe type of network layer security attack called Wormhole attack can occur in MANET, during which a malicious node captures packets from one location in the network, and tunnels them to another colluding malicious node at a distant point, which replays them locally. This paper presents a hierarchical cluster based Wormhole attack avoidance technique to avoid such scenario. The concept of hierarchical clustering with a novel hierarchical 32-bit node addressing scheme is used for avoiding the attacking path during the route discovery phase of the DSR protocol, which is considered as the under lying routing protocol. Pinpointing the location of the Wormhole nodes in the case of exposed attack is also given by using this method

Interactional motifs: Leveraging risks in large and distributed software development teams

tru