Faculty Publications
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Publications by NITK Faculty
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Item Time stamp based set covering greedy algorithm(Association for Computing Machinery acmhelp@acm.org, 2015) Vasanthi, P.; Tejaswi, V.; Santhi Thilagam, P.Influence maximization deals with finding a small set of target nodes that can be initially activated, such that the influence spread beginning with this causes maximum number of expected activated nodes in the network. Most of the existing algorithms for choosing the seed set concentrate only on the structural properties of the network. We would like to emphasize that it is equally important that a user should be actively involved with his neighbours in order to successfully influence them. Hence a novel measure termed as 'Activeness' of a user which is based on timestamp of the user's recent communication with his neighbours is considered. On the same lines, we propose time stamp based set covering greedy (TSCG) algorithm for seed set selection and a Time stamp based threshold model to map the information diffusion in the network. As a part of our experiments, we compare and analyse the results with degree centrality measure and set covering greedy(SCG) algorithm and cite that the spread achieved by our proposed algorithm though lesser in some cases, is more accurate. © 2015 ACM.Item Diffusion models and approaches for influence maximization in social networks(Institute of Electrical and Electronics Engineers Inc., 2016) Tejaswi, V.; Bindu, P.V.; Santhi Thilagam, P.S.Social Network Analysis (SNA) deals with studying the structure, relationship and other attributes of social networks, and provides solutions to real world problems. Influence maximization is one of the significant areas in SNA as it helps in finding influential entities in online social networks which can be used in marketing, election campaigns, outbreak detection, and so on. It deals with the problem of finding a subset of nodes called seeds such that it will eventually spread maximum influence in the network. This paper focuses on providing a complete survey on the influence maximization problem and covers three major aspects: i) different types of input required ii) influence propagation models that map the spread of influence in the network, and iii) the approximation algorithms suggested for seed set selection. We also provide the state of the art and describe the open problems in this domain. © 2016 IEEE.Item Target specific influence maximization: An approach to maximize adoption in labeled social networks(Institute of Electrical and Electronics Engineers Inc., 2017) Tejaswi, V.; Bindu, P.V.; Santhi Thilagam, P.S.Influence maximization deals with finding a small set of nodes, called seed set, to be initially influenced such that they will eventually spread the influence to maximum number of users in the social network. This paper deals with a specialization of the basic problem called labeled influence maximization that identifies seeds that will maximize the influence spread among a specific set of target users identified by their attribute values. In a social setting, a large difference exists between awareness and adoption of an idea/product. This notion fits well in case of labeled influence maximization where any user can become 'aware' about a product whereas only specific users 'adopt' the product. This work considers the problem of labeled influence maximization by incorporating the difference between awareness and adoption. Due to the inherent difference in nature, characteristics, and interests of every user, the number of users who adopt a product varies depending on the type of users in the network and the suitability of the product being marketed. Most of the existing diffusion models do not take this into account. This paper proposes a target adoption model that accounts for both awareness and adoption spread in the network, and a heuristic based discounting approach to find the seed set. The proposed approach is evaluated on different datasets and found to outperform the existing heuristics and discounting approaches. The approach causes maximum adoption in the given social network. © 2017 IEEE.