Name
Abstract | ||
|---|---|---|
In many complex networked systems such as online social networks, at any given time, activity originates at certain nodes and subsequently spreads on the network through influence. Under such scenarios, influencer mining does not involve explicit seeding as in the case of viral marketing. Being an influencer necessitates creating content and disseminating the same to active followers who can then spread the same on the network. In this work, we present a simple probabilistic formulation that models such self-evolving systems where information diffusion occurs primarily because of the intrinsic activity of users and the spread of activity occurs due to influence. We provide an algorithm to mine for the influential seeds in such a scenario by modifying the well-known influence maximization framework with the independent cascade diffusion model. A small example is provided to illustrate how the incorporation of intrinsic and influenced activation mechanisms help us better model the influence dynamics in social networks. Following that, for a larger dataset, we compare the lists of influential users identified by the given formulation with a computationally efficient centrality metric derived from a linear probabilistic model that incorporates self activation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-47874-6_10 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Complex networks,Influence maximization,Social influence,Self-activation,Centrality,Spectral methods | Data mining,Mathematical optimization,Viral marketing,Social network,Computer science,Centrality,Dissemination,Statistical model,Complex network,Probabilistic logic,Maximization | Conference |
Volume | ISSN | Citations |
10047 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arun V. Sathanur | 1 | 17 | 6.10 |
| Mahantesh Halappanavar | 2 | 218 | 33.64 |