Faculty Publications
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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.Item Identifying Provenance of Information and Anomalous Paths in Attributed Social Networks(Institute of Electrical and Electronics Engineers Inc., 2018) Trivedi, H.; Bindu, P.V.; Santhi Thilagam, P.S.Information provenance problem is an important and challenging problem in social network analysis and it deals with identifying the origin or source of information spread in a social network. In this paper, an approach for detecting the source of an information spread as well as suspicious anomalous paths in a social network is proposed. An anomalous path is a sequence of nodes that propagates an anomalous information to the given destination nodes who cause an anomalous event. The proposed approach is based on attribute-based anomalies and information cascading technique. The anomalous paths are identified in two steps. The first step assigns an anomalous score to each and every vertex in the given graph based on suspicious attributes. The second step detects the source and suspicious anomalous paths in the network using the anomaly scores. The approach is tested on datasets such as Enron and Facebook to demonstrate its effectiveness. Detecting anomalous paths is useful in several applications including identifying terrorist attacks communication path, disease spreading pattern, and match-fixing hidden path between bookie and a cricketer. © 2018 IEEE.Item Influence maximisation in social networks(Inderscience Publishers, 2019) Tejaswi, V.; Bindu, P.V.; Santhi Thilagam, P.S.Influence maximisation is one of the significant research areas in social network analysis. It helps in identifying influential entities from social networks that can be used in marketing, election campaigns, outbreak detection and so on. Influence maximisation deals with the problem of finding a subset of nodes called seeds in the social network such that these nodes will eventually spread maximum influence in the network. This is an NP-hard problem. The aim of this paper is to provide a complete understanding of the influence maximisation problem. This paper focuses on providing an overview on the influence maximisation problem, and covers three major aspects: 1) different types of inputs required; 2) influence propagation models that map the spread of influence in the network; 3) the approximation algorithms proposed for seed set selection. In addition, we provide the state of the art and describe the open problems in this domain. © 2019 Inderscience Enterprises Ltd.