Pre-Conference Talk by LUU Minh Duc

Diffusion is an important dynamics that helps spreading information within an online social network. While there are already numerous models forsingle item diffusion, few have studied diffusion of multiple items, especiallywhen items can interact with one another due to their inter-similarity. Moreover,the well-known homophily effect is rarely considered explicitly in the existingdiffusion models. This work therefore fills this gap by proposing a novel modelcalled Topic level Interaction Homophily Aware Diffusion (TIHAD) to includeboth latent factor level interaction among items and homophily factor in diffusion.The model determines item interaction based on latent factors and edge strengthsbased on homophily factor in the computation of social influence. An algorithmor 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 studyon hashtag diffusion in Twitter also shows that TIHAD outperforms the baseline model in the hashtag adoption prediction task.

This is a pre-conference talk for European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML PKDD 2015).