Faculty Publications
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Item A novel hybrid algorithm for overlapping community detection in social network using community forest model and nash equilibrium(Springer Verlag service@springer.de, 2019) Sarswat, A.; Guddeti, R.M.R.Overlapping community detection in social networks is known to be a challenging and complex NP-hard problem. A large number of heuristic approaches based on optimization functions like modularity and modularity density are available for community detection. However, these approaches do not always give an optimum solution, and none of these approaches are able to clearly provide a stable overlapping community structure. Hence, in this paper, we propose a novel hybrid algorithm to detect the overlapping communities based on the community forest model and Nash equilibrium. In this work, overlapping community has been detected using backbone degree and expansion of the community forest model, and then a Nash equilibrium is found to get a stable state of overlapping community arrangement. We tested the proposed hybrid algorithm on standard datasets like Zachary’s karate club, football, etc. Our experimental results demonstrate that the proposed approach outperforms the current state-of-the-art methods in terms of quality, stability, and less computation time. © Springer Nature Singapore Pte Ltd. 2019Item Fast Convergence to Near Optimal Solution for Job Shop Scheduling Using Cat Swarm Optimization(Springer Verlag service@springer.de, 2017) Dani, V.; Sarswat, A.; Swaroop, V.; Domanal, S.; Guddeti, G.R.M.Job Shop Scheduling problem has wide range of applications. However it being a NP-Hard optimization problem, always finding an optimal solution is not possible in polynomial amount of time. In this paper we propose a heuristic approach to find near optimal solution for Job Shop Scheduling Problem in predetermined amount of time using Cat Swarm Optimization. Novelty in our approach is our non-conventional way of representing position of cat in search space that ensures advantage of spatial locality is taken. Further while exploring the search space using randomization, we never explore an infeasible solution. This reduces search time. Our proposed approach outperforms some of the conventional algorithms and achieves nearly 86% accuracy, while restricting processing time to one second. © 2017, Springer International Publishing AG.Item A novel overlapping community detection using parallel CFM and sequential nash equilibrium(Institute of Electrical and Electronics Engineers Inc., 2018) Sarswat, A.; Guddeti, R.M.Detecting Overlapping Community in Social Networks is one of the challenging and complex problem. Several approaches based on heuristic, modularity & modularity density, graph partitioning and game theory are available for community detection. However getting an optimum and stable solution with less computation cost for large datasets is not possible using these existing approaches. Hence, in this work, we propose a novel overlapping community detection algorithm based on parallel community forest model and sequential Nash Equilibrium for large datasets. In this paper, community forest model (CFM) is implemented in parallel using Spark framework to get the initial community structure and then a Nash Equilibrium is computed to find a stable overlapping community structure. We conducted experiments on the benchmark LFR dataset with different sizes like 500, 1000, 2000 upto 10,000 nodes to evaluate the proposed method. Our experimental results clearly demonstrate that the proposed approach outperforms the existing works in terms of quality, scalability, stability and less computation time. © 2018 IEEE.Item A novel two-step approach for overlapping community detection in social networks(Springer-Verlag Wien michaela.bolli@springer.at, 2017) Sarswat, A.; Jami, V.; Guddeti, G.With the rapid increase in popularity of online social networks, community detection in these networks has become a key aspect of research field. Overlapping community detection is an important NP-hard problem of social network analysis. Modularity-based community detection is one of the most widely used approaches for social network analysis. However, modularity-based community detection technique may fail to resolve small-size communities. Hence, we propose a novel two-step approach for overlapping community detection in social networks. In the first step, modularity density-based hybrid meta-heuristics approach is used to find the disjoint communities and the quality of these disjoint communities can be verified using Silhouette coefficient. In the second step, the quality disjoint communities with low computation cost are used to detect overlapping nodes based on Min-Max Ratio of minimum(indegree, outdegree) to the maximum(indegree, outdegree) values of nodes. We tested the proposed algorithm based on 10 standard community quality metrics along with Silhouette score using seven standard datasets. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art works in terms of quality and scalability. © 2017, Springer-Verlag GmbH Austria.