Publication Type

Conference Proceeding Article

Publication Date

9-2015

Abstract

Diffusion is an important dynamics that helps spreading information within an online social network. While there are already numerous models for single item diffusion, few have studied diffusion of multiple items, especially when items can interact with one another due to their inter-similarity. Moreover, the well-known homophily effect is rarely considered explicitly in the existing diffusion models. This work therefore fills this gap by proposing a novel model called Topic level Interaction Homophily Aware Diffusion (TIHAD) to include both latent factor level interaction among items and homophily factor in diffusion. The model determines item interaction based on latent factors and edge strengths based on homophily factor in the computation of social influence. An algorithm for training TIHAD model is also proposed. Our experiments on synthetic and real datasets show that: (a) homophily increases diffusion significantly, and (b) item interaction at topic level boosts diffusion among similar items. A case study on hashtag diffusion in Twitter also shows that TIHAD outperforms the baseline model in the hashtag adoption prediction task.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II

Volume

9285

First Page

701

Last Page

718

ISBN

9783319235240

Identifier

10.1007/978-3-319-23525-7_43

Publisher

Springer Verlag

City or Country

Cham

Additional URL

http://dx.doi.org/10.1007/978-3-319-23525-7_43

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