Supreet Reddy Mandala

Clustering social networks using ant colony optimization

Several e-marketing applications rely on the ability to understand the structure of social networks. Social networks can be represented as graphs with customers as nodes and their interactions as edges. Most real world social networks are known to contain extremely dense subgraphs (also called as communities) which often provide critical insights about the emergent properties of the social network. The communities, in most cases, correspond to the various segments in a social system. Such an observation led researchers to propose algorithms to detect communities in networks. A modularity measure representing the quality of a network division has been proposed which on maximization yields good partitions. The modularity maximization is a strongly NP-complete problem which renders mathematical programming based optimization intractable for large problem sizes. Many heuristics based on simulated annealing, genetic algorithms and extremal optimization have been used to maximize modularity but have lead to suboptimal solutions. In this paper, we propose an ant colony optimization (ACO) based approach to detect communities. To the best of our knowledge, this is the first application of ACO to community detection. We demonstrate that ACO based approach results in a significant improvement in modularity values as compared to existing heuristics in the literature. The reasons for this improvement when tested on real and synthetic data sets are discussed.

A Game-Theoretic Approach to Graph Clustering

The last decade has witnessed an explosion in the modeling of complex systems. Predominantly, graphs are used to represent these systems. The problem of detecting overlapping clusters in graphs is of utmost importance. We present a novel definition of overlapping clusters. A noncooperative game is proposed such that the equilibrium conditions of the game correspond to the clusters in the graph. Several properties of the game are analyzed and exploited to show the existence of a pure Nash equilibrium NE and compute it effectively. We present two algorithms to compute NE and prove their convergence. Empirically, the running times of both algorithms are nearly linear in the number of edges. Also, one of the algorithms can be readily parallelized, making it scalable. Finally, our approach is compared with existing overlapping cluster detection algorithms and validated on several artificial and real data sets.

Hybrid Role Mining for Security Service Solution

IT services delivery is a complex ecosystem that engages 100000s of system administrators in service delivery centers globally managing 1000s of IT systems on behalf of customers. Such large-scale hosting environments require a flexible identity management system to provision necessary access rights, in order to ensure compliance posture of an organization. A popular and effective access control scheme is Role Based Access Control (RBAC). Ideally, a role should correspond to a business function performed within an enterprise. Several role mining algorithms have been proposed which attempt to automate the process of role discovery. In this paper, we represent the user-permission assignments as a bi-partite graph with users/permissions as vertices and user-permission assignments as edges. Given a user-permission bi-partite graph, most role mining algorithms focus on discovering roles that cover all the user-permission assignments. We show that by relaxing the coverage requirement, one can improve the accuracy of role detection. We propose a parameterized definition of a role based on graph theoretical properties, and demonstrate that the role parameters can be controlled to balance the accuracy and coverage of the roles detected. Finally, we propose a heuristic to illustrate the efficacy of our approach and validate it on real and artificial organizational access control data.