Embedding Learning with Events in Heterogeneous Information Networks

Abstract

In real-world applications, objects of multiple types are interconnected, forming Heterogeneous Information Networks. In such heterogeneous information networks, we make the key observation that many interactions happen due to some event and the objects in each event form a complete semantic unit. By taking advantage of such a property, we propose a generic framework called HyperEdge-Based Embedding (Hebe) to learn object embeddings with events in heterogeneous information networks, where a hyperedge encompasses the objects participating in one event. The Hebe framework models the proximity among objects in each event with two methods: (1) predicting a target object given other participating objects in the event, and (2) predicting if the event can be observed given all the participating objects. Since each hyperedge encapsulates more information of a given event, Hebe is robust to data sparseness and noise. In addition, Hebe is scalable when the data size spirals. Extensive experiments on large-scale real-world datasets show the efficacy and robustness of the proposed framework.

Efficient Ridesharing Framework for Ride-matching via Heterogeneous Network Embedding

“…Under the HIN-based representation, the ride-matching problem can be considered a similar participant search task over the HIN. Many works have been devoted to mining heterogeneous networks in the past few years [27][28][29][30] to study such networks with multiple types of nodes and links, i.e., heterogeneous network embedding has been proposed, as well as studies like metapath2vec, Predictive text embedding (PTE) [31], and Embedding of Embedding (EOE) [32]. Some existing network embedding methods are applied in e-commence, e.g., to identify the enterprise customer [33] and to recommend items [34]; as well as bibliographic, e.g., to identify the author-article [35] and to label the article [36]; and social networks [37].…”

Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

“…A heterogeneous network can represent information about different types of nodes, as well as relationships between nodes. The PTE model achieves network heterogeneity by classifying text or tags and representing the relationship [28]; the HINES model constructs a heterogeneous network through implementing a representation of paths between nodes according to metainformation [29]; on the basis of edge features and the superboundary concept, the authors in [30,31] proposed the HEBE embedded framework to model events with strong correlation as a whole and realize a heterogeneous event network. However, a big drawback of heterogeneous networks is to build accurate metapaths when representing relationships between nodes, while specific metapaths constrain heterogeneous networks within the framework of a particular network.…”

Feature-rich networks: going beyond complex network topologies

“…These constraints make a heterogeneous information network semi-structured, guiding the exploration of the semantics of the network ). This HIN model has been successfully used for several mining tasks, such us ranking-based clustering combinations ), transductive and ranking-based classification (Ji et al 2010;Ji et al 2011), similarity search (Sun et al 2011) and relationship prediction Deng et al 2014), and, more recently, learning of object-event embeddings (Gui et al 2017) and named entity linking (Shen et al 2018). However, the notion of HIN is general enough to include other network models which are inherently heterogeneous in node and relation types, e. g. networks related to the Internet-of-Things (George and Thampi 2018;Misra et al 2012;Qiu et al 2016).…”