Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
On spreading recommendations via social gossip
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Determining the top-k nodes in social networks using the Shapley value
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
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Models for the processes by which ideas and influences propagate through a social network have been studied in number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of ’word of mouth’ in the promotion of new products. The problem of selecting a set of most influential nodes in a network has been proved to be NP-hard. We propose a framework to analyze the network in depth and to find the set of most influential nodes. We consider the problem of selecting, for any given positive integer k, the most influential k nodes in a Academic Social Network (ASN), based on certain criterions relevant in academic environment like number of citations, working location of authors, cross reference and cross co-authorship. Based on the initial node set selection and the diffusion model, we study the spread of influence of the influential nodes in the academic network. Appropriate criterions are used in the proposed generalized diffusion models. In this paper, we used two different models; (1) Linear Threshold Model and (2) Independent Cascade model to find the set of influential nodes for different criterions in an ASN and compared their performances. We constructed ASN based on the information collected from DBLP, Citeseer and used Java Social Network Simulator (JSNS) for experimental simulations.