2,549 research outputs found
Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models
The steady growth of graph data from social networks has resulted in
wide-spread research in finding solutions to the influence maximization
problem. In this paper, we propose a holistic solution to the influence
maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI)
model that closely mirrors the real-world scenarios. Under the OI model, we
introduce a novel problem of Maximizing the Effective Opinion (MEO) of
influenced users. We prove that the MEO problem is NP-hard and cannot be
approximated within a constant ratio unless P=NP. (2) We propose a heuristic
algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM
heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a
scalable algorithm capable of running within practical compute times on
commodity hardware. In addition to serving as a fundamental building block for
OSIM, EaSyIM is capable of addressing the scalability aspect - memory
consumption and running time, of the IM problem as well.
Empirically, our algorithms are capable of maintaining the deviation in the
spread always within 5% of the best known methods in the literature. In
addition, our experiments show that both OSIM and EaSyIM are effective,
efficient, scalable and significantly enhance the ability to analyze real
datasets.Comment: ACM SIGMOD Conference 2016, 18 pages, 29 figure
Modelling of trends in Twitter using retweet graph dynamics
In this paper we model user behaviour in Twitter to capture the emergence of
trending topics. For this purpose, we first extensively analyse tweet datasets
of several different events. In particular, for these datasets, we construct
and investigate the retweet graphs. We find that the retweet graph for a
trending topic has a relatively dense largest connected component (LCC). Next,
based on the insights obtained from the analyses of the datasets, we design a
mathematical model that describes the evolution of a retweet graph by three
main parameters. We then quantify, analytically and by simulation, the
influence of the model parameters on the basic characteristics of the retweet
graph, such as the density of edges and the size and density of the LCC.
Finally, we put the model in practice, estimate its parameters and compare the
resulting behavior of the model to our datasets.Comment: 16 pages, 5 figures, presented at WAW 201
Birds of a Feather Talk Together: User Influence on Language Adoption
Language is in constant flux be it from changes in meaning to the introduction of new terms. At the user level it changes by users accommodating their language in relation to whom they are in contact with. By mining diffusion's of new terms across social networks we detect the influence between users and communities. This is then used to compute the user activation threshold at which they adopt new terms dependent on their neighbours. We apply this method to four different networks from two popular on-line social networks (Reddit and Twitter). This research highlights novel results: by testing the network through random shuffles we show that the time at which a user adopts a term is dependent on the local structure, however, a large part of the influence comes from the global structure and that influence between users and communities is not significantly dependent on network structures
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