Learning influence probabilities in social networks

By Amit Goyal, Francesco Bonchi, Laks V. S. Lakshmanan. 2010

1,146 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

DeepInf: Social Influence Prediction with Deep Learning (2018, background): “Examples of such work include pairwise influence [19, 39], topic-level influence [42], group formation [2, 38] and structural diversity [14, 29, 46].”
Topological Recurrent Neural Network for Diffusion Prediction (2017, background): “The results also show that, by learning a dynamic sender embedding, Topo-LSTM can better capture the complex diffusion dynamics and interactions among active nodes, which are important to activation prediction, than Embedded-IC, IC-SB, and DeepWalk, which either learn static representations of activ”
Continuous-Time Dynamic Network Embeddings (2018, background): “Temporal networks have been the focus of recent research including node classification in temporal networks [57], temporal link prediction [19], dynamic community detection [16], and dynamic mixed-membership role models [22, 59], anomaly detection in dynamic networks [55], influence modeling and onl”
Community Detection and Mining in Social Media (2010, background): “, subscribing to different interest groups, commenting on a photo) (Goyal et al., 2010; Saito et al., 2010) may be available.”
Influence Maximization on Social Graphs: A Survey (2018, background): “Recently, some studies propose to learn inﬂuence probabilities from data, e.g., propagation actions (e.g., replies, forwards, etc.) in the social networks [22], [23], [24], [25].”
Social Network Data Analytics (2011, methodology): “The static component in [13] applies this algorithm upon the graph of interactions, as recorded within the current time window: interactions that have moved outside the window are forgotten, and hence nodes may become disconnected, while new connections may emerge.”
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems (2019, background): “is observation could be justied by one argument in sociology research that inuence from users always propagates to adjacent users in social networks [7].”
Recommender Systems (2016, neutral): “One of the main problems in social influence models is a difficulty in learning the influence propagation probabilities, though there has been some recent focus on this issue [234].”
Influence maximization: near-optimal time complexity meets practical efficiency (2014, background): “, the propagation probability on each edge) from observed data [10, 21, 27].”
Analysis of Physical Activity Propagation in a Health Social Network (2014, background): “Another line of study has focused on the problem of learning the inﬂuence probabilities on every edge of a social network given an observed log of propagations over this network [4, 21, 24, 28].”
The role of social networks in information diffusion (2012, background): “Diffusion and influence have been modeled in blogs [2, 20, 25], email [31], and sites such as Twitter, Digg, and Flickr [8, 21, 29].”
Topic-aware social influence propagation models (2012, neutral): “Suppose we are also given the estimates of reciprocal influence between effort has been devoted to develop methods for improving the efficiency of influence maximization [6], [7], [8], [12].”
Modeling Information Diffusion in Implicit Networks (2010, background): “Only recently has the availability of large social network and corresponding diffusion data made it possible to estimate such models in practice [14], [30].”
A Data-Based Approach to Social Influence Maximization (2011, background): “Moreover, infl(u) denotes the user influenceability, that is, how prone the user u is to influence by the social context [7].”