(2) how to leverage the correlation and the network information to help build the graphical model?

    We design two categories of factor graph models. The first category is for unsupervised learning. We have proposed Topical Factor Graph (TFG) (Tang et al., KDD'09), Time-constrained Probabilistic Factor Graph model (TPFG) (Wang et al., KDD'10). The second category is for supervised learning. We have proposed Partially Labeled Factor Graph model (PLFG) (Zhuang et al., DMKD'12), Triad-based Factor Graph model (TriFG) (Lou et al., TKDD'12), Transfer-based Factor Graph model (TranFG) (Tang et al., WSDM'12).



Related codes:

[Partially Labeled Factor Graph  readme. Refer to Web page or Tang et al. PKDD'11 for details.]

[Topic Affinity Propagation. Refer to Web Page or Tang et al. KDD'09 for details.]

In online social networks, social relationship is the most basic unit to form the network structure. Relationships between users can be either directed or undirected.

    We focus on studying two aspects of social tie: (1) to which extent the label of social ties between people can be inferred in social networks? (2) how reciprocal (two-way) relationships are developed from parasocial (one-way) relationships and how the relationships further develop into triadic closure and communities? (3) how communities dynamically evolve over time?

    We propose a unsupervised dynamic factor graph model to infer advisor-advisee relationship from the coauthor network (Wang et al., KDD'10) and a partially labeled factor graph model to infer the type of social relationships (Tang et al., ECML/PKDD'11, PKDD Best Student Paper Runnerup). We further incoporate social theories (e.g., social balance theory, social status theory, structural hole theory, two-step flow theory, and strong/weak tie) into a triad-based factor graph model to infer the formation of reciprocal relationships from parasocial relationships (Hopcroft et al., CIKM'11) and to infer the formation of triad closure (Lou et al., TKDD'13), and leverage features defined based on those social theories to infer social ties across heterogeneous networks (Tang et al., WSDM'12). We have further proposed a co-evolution model for modeling the dynamic changes of communities in social networks (Sun et al., TKDE'13).



Related data sets and codes: [Social-Tie]  [Reciprocity&Triadic Closure]

Invited talks were given at different venues and related slides can be downloaded here. [PDF]

    The theory of structural holes suggests that individuals would benefit from filling the ``holes'' (called as structural hole spanners) between people or groups that are otherwise disconnected.

    The fundamental challenge we want to address is to detect users who span structural holes in social networks and how the structural hole spanners influence the information diffusion?

    We explore the problem of mining structural hole spanners through information diffusion in social networks (Lou and Tang, WWW'13). We precisely define the problem of mining top-k structural hole spanners in large-scale social networks and provide an objective (quality) function to formalize the problem. Two instantiation models (HIS and MaxD) have been developed to implement the objective function. The optimization is proved to be NP-hard, and we design an efficient algorithm with provable approximation guarantees.

   

Related data sets and codes: [Structural hole&Information diffusion]