Mining heterogeneous information networks: a structural analysis approach

By Yizhou Sun, Jiawei Han. SKDD, 2013

648 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8522449394d038f17a48517e4eb12f62cfd5e361

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Citations in the corpus

Multilayer networks (2013, background): “Some of aforementioned multilayer networks possess an additional constraint: if two edges have the same color, then the pair of nodes that are incident to one of these edges must share the same color combination as the pair of nodes that are incident to the other edge [99, 140, 324, 325].”
Heterogeneous Graph Attention Network (2019, background): “learning. 3 PRELIMINARY A heterogeneous graph is a special kind of information network, which contains either multiple types of objects or multiple types of links. Definition 3.1. Heterogeneous Graph [31]. A heterogeneous graph, denoted as G= (V,E), consists of an object set Vand a link set E. A het”
Entity Embedding-Based Anomaly Detection for Heterogeneous Categorical Events (2016, background): “However, our setting is different in the sense that, as shown in Section 2, when treating categorical events as a network, it is a heterogeneous network [Sun and Han, 2013].”
Representation Learning for Dynamic Graphs: A Survey (2019, background): “A heterogeneous information network (HIN) is typically defined as a digraph G = (V, E) with two additional functions: one mapping each node v ∈ V to a node type and one mapping each edge (vi, vj) ∈ E to an edge type (Shi et al. (2016); Sun and Han (2013)).”
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020, neutral): “HETEROGENEOUS graphs (HGs) [1], which are capable of composing different types of entities (i.”
Structural Neighborhood Based Classification of Nodes in a Network (2016, background): “A heterogeneous information network [20] is a network composed of multiple types of nodes and relationships be-tween them.”
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (2017, background): “Heterogeneous information networks (HINs) [30] have been proposed as a general data representation for many different types of data, such as scholar network data [32], social network data [13], patient network data [3], or knowledge graph data [5].”
OAG: Toward Linking Large-scale Heterogeneous Entity Graphs (2019, background): “HeterogeneousEntityGraph(HEG) (alsoknown as the heterogeneous information network (HIN) [28]).”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, methodology): “twork (HIN) is typically dened as a digraph G= (V;E) with two additional functions: one mapping each node v 2Vto a node type and one mapping each edge (v i;v j) 2Eto an edge type (Shi et al. (2016); Sun and Han (2013)). Compared to KGs, HINs dene node types explicitly using a mapping function wherea”