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Suman Kundu

Bio: Suman Kundu is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Social network & Granular computing. The author has an hindex of 5, co-authored 10 publications receiving 190 citations. Previous affiliations of Suman Kundu include Indian Institutes of Technology & Indian Institute of Technology, Jodhpur.

Papers
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Book ChapterDOI
27 Jun 2011
TL;DR: A centrality measure for independent cascade model, which is based on diffusion probability (or propagation probability) anddegree centrality and degree centrality is proposed, which indicates, k nodes obtained through (i) significantly outperform those obtain by (ii) and (iii).
Abstract: The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose a centrality measure for independent cascade model, which is based on diffusion probability (or propagation probability) and degree centrality. We use (i) centrality based heuristics with the proposed centrality measure to get k influential individuals. We have also found the same using (ii) high degree heuristics and (iii) degree discount heuristics. A Monte-Carlo simulation has been conducted with top k-nodes found through different methods. The result of simulation indicates, k nodes obtained through (i) significantly outperform those obtain by (ii) and (iii). We further verify the differences statistically using T-Test and found the minimum significance level (p-value) when k > 5 is 0.022 compare with (ii) and 0.015 when comparing with (iii) for twitter data.

69 citations

Journal ArticleDOI
TL;DR: Experimental results on benchmark data show the superiority of the proposed community detection algorithm compared to other well known methods, particularly when the network contains overlapping communities.

47 citations

Journal ArticleDOI
TL;DR: Two new centrality measures, Diffusion Degree for independent cascade model of information diffusion and Maximum Influence Degree are proposed, which provide the maximum theoretically possible influence Upper Bound for a node.
Abstract: The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose two new centrality measures, Diffusion Degree for independent cascade model of information diffusion and Maximum Influence Degree. Unlike other existing centrality measures, diffusion degree considers neighbors' contributions in addition to the degree of a node. The measure also works flawlessly with non uniform propagation probability distributions. On the other hand, Maximum Influence Degree provides the maximum theoretically possible influence Upper Bound for a node. Extensive experiments are performed with five different real life large scale directed social networks. With independent cascade model, we perform experiments for both uniform and non uniform propagation probabilities. We use Diffusion Degree Heuristic DiDH and Maximum Influence Degree Heuristic MIDH, to find the top k influential individuals. k seeds obtained through these for both the setups show superior influence compared to the seeds obtained by high degree heuristics, degree discount heuristics, different variants of set covering greedy algorithms and Prefix excluding Maximum Influence Arborescence PMIA algorithm. The superiority of the proposed method is also found to be statistically significant as per T-test.

44 citations

Journal ArticleDOI
TL;DR: A novel modeling technique based on granular computing theory and fuzzy neighborhood systems, which provides a uniform framework to represent social networks, named Fuzzy Granular Social Network (FGSN).

36 citations

Journal ArticleDOI
TL;DR: It is theoretically proved that for any monotonic and sub-modular influence function, the algorithm correctly identifies the nodes to be deprecated and for any finite set of input nodes it is shown that the algorithm can meet the ending criteria.

29 citations


Cited by
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Proceedings Article
01 Jan 2000
TL;DR: Clusters---a grouping of clients that are close together topologically and likely to be under common administrative control are introduced, using a ``network-aware" method, based on information available from BGP routing table snapshots.
Abstract: Being able to identify the groups of clients that are responsible for a significant portion of a Web site's requests can be helpful to both the Web site and the clients. In a Web application, it is beneficial to move content closer to groups of clients that are responsible for large subsets of requests to an origin server. We introduce clusters---a grouping of clients that are close together topologically and likely to be under common administrative control. We identify clusters using a ``network-aware" method, based on information available from BGP routing table snapshots.

284 citations

Journal ArticleDOI
TL;DR: An overview on Big Data is presented including four issues, namely: concepts, characteristics and processing paradigms of Big data; the state-of-the-art techniques for decision making in Big Data; felicitous decision making applications of Big Data in social science; and the current challenges ofBig Data as well as possible future directions.

203 citations

01 Nov 2010
TL;DR: It is found that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time, suggesting that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight.
Abstract: Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.

174 citations

Journal ArticleDOI
Maoguo Gong1, Jianan Yan1, Bo Shen1, Lijia Ma1, Qing Cai1 
TL;DR: In this study, an optimization model based on a local influence criterion is established for the influence maximization problem and a discrete particle swarm optimization algorithm is proposed to optimize theLocal influence criterion.

173 citations

Journal ArticleDOI
TL;DR: CoFIM is proposed, a community-based framework for influence maximization on large-scale networks that derives a simple evaluation form of the total influence spread which is submodular and can be efficiently computed and a fast algorithm to select the seed nodes.
Abstract: Influence maximization is a classic optimization problem studied in the area of social network analysis and viral marketing. Given a network, it is defined as the problem of finding k seed nodes so that the influence spread of the network can be optimized. Kempe et al. have proved that this problem is NP hard and the objective function is submodular, based on which a greedy algorithm was proposed to give a near-optimal solution. However, this simple greedy algorithm is time consuming, which limits its application on large-scale networks. Heuristic algorithms generally cannot provide any performance guarantee. To solve this problem, in this paper we propose CoFIM, a community-based framework for influence maximization on large-scale networks. In our framework the influence propagation process is divided into two phases: (i) seeds expansion; and (ii) intra-community propagation. The first phase is the expansion of seed nodes among different communities at the beginning of diffusion. The second phase is the influence propagation within communities which are independent of each other. Based on the framework, we derive a simple evaluation form of the total influence spread which is submodular and can be efficiently computed. Then we further propose a fast algorithm to select the seed nodes. Experimental results on synthetic and nine real-world large datasets including networks with millions of nodes and hundreds of millions of edges show that our algorithm achieves competitive results in influence spread as compared with state-of-the-art algorithms and it is much more efficient in terms of both time and memory usage.

139 citations