Social influence analysis in large-scale networks

By Jie Tang, Jimeng Sun, Chi Wang, Zi Yang. Knowledge Discovery and Data Mining, 2009

1,155 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b0b08e739aa5a674430c54e4e7e540ce81ea5b2a

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

In Statistical and Nonlinear Physics

Citations in the corpus

Beyond Low-frequency Information in Graph Convolutional Networks (2021, methodology): “We consider the Wikipedia networks1 and Actor co-occurrence network (Tang et al. 2009) for disassortative datasets.”
Learning content–social influential features for influence analysis (2016, positive): “[30] considered a social network and a prior topic distribution as input, then learned user-pair topic-special influence strength, which does not consider simultaneous learning of topics and influence, nor content propagation process.”
Vital nodes identification in complex networks (2016, background): “While if the two volunteers spreading different messages, the comparison cannot be completely fair since the prevalence of a message is very sensitive to its content [378, 379, 380].”
Dynamics of information diffusion and its applications on complex networks (2016, background): “Computer scientists regard both static network topologies and dynamic flows as independent features, and optimize the objective function via iterative refinement [283].”
Learning influence probabilities in social networks (2010, background): “Tang et al. [ 19 ] introduce the problem of topic-based social influence analysis.”
Harvesting ambient geospatial information from social media feeds (2011, methodology): “It should be noted here that the degree of inﬂuence that a user has within a given network has been proven to vary with the particular topic under examination (see Tang et al. 2009).”
Big data related technologies, challenges and future prospects (2015, neutral): “in [45] proposed a system to automatically”
Scalable Influence Maximization in Social Networks under the Linear Threshold Model (2010, background): “Several studies design machine learning algorithms to extract influence cascade model parameters from real datasets [11], [12], [13], [14], which can be used to generate influence graphs studied in this paper.”
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily (2022, background): “Actor is a graph induced from the film-director-actor-writer network in (Tang et al., 2009), which describes the co-occurrence relation between actors in Wikipedia pages.”
Recommender Systems (2012, background): “Social influences may also vary across topics and items: [227] showed that different tags and topics spread on Twitter differently and [228] found that strength and direction of social influence are topic-dependent.”
Topic-aware social influence propagation models (2012, neutral): “To the best of our knowledge, only few papers have looked at social influence from the topics’ perspective [17,18,28,29].”
Geom-GCN: Geometric Graph Convolutional Networks (2020, neutral): “This dataset is the actor-only induced subgraph of the film-directoractor-writer network (Tang et al., 2009).”
Churn in Social Networks (2010, background): “There has been some recent work in this direction for determining different influence measures for different topics [64] and movies [23], which we plan to explore for determining relative influence of roles.”
Recommender Systems (2016, neutral): “Other recent models for influence analysis in social networks are discussed in [153, 154, 369, 589].”
Understanding over-squashing and bottlenecks on graphs via curvature (2021, methodology): “Chameleon and Squirrel (Rozemberczki et al., 2021) are medium heterophilic datasets based on Wikipedia networks, along with Actor, the actor-only induced subgraph of the filmdirector-actor-writer network (Tang et al., 2009).”
Propagation Model Search for Graph Neural Networks (2020, background): “…are a ubiquitous form of modeling complex interactions among individual entities and have a broad range of applications in social network analysis (Tang et al. 2009), recommender systems (Monti, Bronstein, and Bresson 2017), drug discovery (Gilmer et al. 2017), etc. Graph neural networks (GNNs)…”
Session-Based Social Recommendation via Dynamic Graph Attention Networks (2019, neutral): “This is known as social influence [32].”