Ranking-based clustering of heterogeneous information networks with star network schema

By Yizhou Sun, Yintao Yu, Jiawei Han. Knowledge Discovery and Data Mining, 2009

553 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/db9d907f127629b6719b8acf6ac51bb87e355803

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In Artificial Intelligence

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Microscopic Social Influence (2012, background): “More recently, a few works have started to take account of the rich information conveyed by the interactions between multiple objects and users, to detect communities [19], to analyze the evolution of both object and social networks [20], or to model topic-sensitive social inﬂuence [21].”
metapath2vec: Scalable Representation Learning for Heterogeneous Networks (2017, methodology): “, skip-gram) to heterogeneous networks? By solving these challenges, the latent heterogeneous network embeddings can be further applied to various network mining tasks, such as node classication [13], clustering [27, 28], and similarity search [26, 35].”
Personalized entity recommendation: a heterogeneous information network approach (2014, background): “A lot of information network mining and learning tasks have been done in the past couple of years, including clustering [25] [26], classification [12], and link prediction [28] [16], etc.”
ClusCite: effective citation recommendation by information network-based clustering (2014, methodology): “Similar ideas were explored when performing clustering [23] and classification [13] in heterogeneous information networks, where object relative authority served as features for representing classes.”
Community Detection and Mining in Social Media (2010, background): “Besides social media, multi-mode networks are also observed in other domains such as information networks (Sun et al., 2009) from DBLP Bibliography1 involving authors, papers and publication venues, or the Internet Movie Database2 involving movies, actors, producers and directors.”
Heterogeneous Information Network Embedding for Recommendation (2017, unknown): “Definition 2 (Network schema [31], [61]).”
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks (2013, background): “Particularly, with the advent of study on heterogeneous information networks [5], [6], it is not only increasingly i mportant but also feasible to study the relatedness among objects with different types.”
Mining heterogeneous information networks: a structural analysis approach (2013, background): “, RankClus [19] and NetClus [22]) that generates both clustering and ranking results efficiently.”
Multilayer networks (2013, background): “X Any 1 [327] Meta-matrix, meta-network Any 2 [78,79,336]”
On truth discovery in social sensing: A maximum likelihood estimation approach (2012, methodology): “Additionally, trust analysis was done both on a homogeneous network [5, 34] and a heterogeneous network [27].”
Graph Regularized Transductive Classification on Heterogeneous Information Networks (2010, methodology): “It has been recognized [8] [16] that while mining heterogeneous information networks, the type differences among links and objects should be respected in order to generate more meaningful results.”
Outlier Analysis (2013, methodology): “The ENetClusmethod [192] generalizes the probabilistic NetClus [435] model to the temporal scenario.”