HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks

Abstract

Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type. However, in many scenarios, we need to measure the relatedness between objects with different types. With the surge of study on heterogeneous networks, the relevance measure on objects with different types becomes increasingly important. In this paper, we study the relevance search problem in heterogeneous networks, where the task is to measure the relatedness of heterogeneous objects (including objects with the same type or different types). A novel measure HeteSim is proposed, which has the following attributes: (1) a uniform measure: it can measure the relatedness of objects with the same or different types in a uniform framework; (2) a path-constrained measure: the relatedness of object pairs are defined based on the search path that connect two objects through following a sequence of node types; (3) a semi-metric measure: HeteSim has some good properties (e.g., self-maximum and symmetric), that are crucial to many data mining tasks. Moreover, we analyze the computation characteristics of HeteSim and propose the corresponding quick computation strategies. Empirical studies show that HeteSim can effectively and efficiently evaluate the relatedness of heterogeneous objects.

ContextCare: Incorporating Contextual Information Networks to Representation Learning on Medical Forum Data

“…However, it is inflexible to link those diseases and symptoms with few neighbors and suffer from the sparseness of symptom-disease links. HeteSim [Shi et al, 2014] learns links between diseases and symptoms by walking on the G SD through the path [disease→disease→symptom→symptom]. However, it is insufficient to deal with latent relations thus suffers from the sparseness of symptom-disease diagnosis network.…”

“…Most of the existing studies [Adamic and Adar, 2003;Jeh and Widom, 2003;Sun et al, 2011;Shi et al, 2014] predict links directly rely on existed links in networks, and these methods are not effective for our problem because disease-symptom networks are very sparse. As shown in experiments, our method outperforms state-of-theart method in this line.…”

Meta-path-based link prediction in schema-rich heterogeneous information network

“…Every two node types can be connected by multiple meta-paths. To use meta-path feature properly, meta-path-based similarity measures are proposed to make meta-path feature quantization and quantify the similarity of nodes [12,16,24] in HIN. Most of the studies or applications of HIN are based on these similarity measures to be performed.…”

“…Lao et al [12] designed a path-constrained random walk (PCRW) algorithm to measure the entity relativity in a labeled directed graph. Shi et al [16] proposed HeteSim to measure relevance of any entity pair under arbitrary meta-path. Although all of these measures can do similarity calculation, not every measure could be used for fast calculation in the process of finding important meta-paths which are symmetric or asymmetric.…”

Geosocial recommender systems

“…Our recommendation extraction work is inspired by Shi et al's HeteRecom [80], which is based on the similarity calculation HeteSim [79]. In their approach, they find a match between users and items using the knowledge 7.3.…”