CHIN: Classification with META-PATH in Heterogeneous Information Networks

Abstract

With the rapid development of digital platforms, users can now interact in endless ways from writing business reviews and comments to sharing information with their friends and followers. As a result, organizations have numerous digital social networks available for graph learning problems with little guidance on how to select the right graph or how to combine multiple edge types. In this paper, we first describe the types of user-to-user networks available across the Facebook (FB) and Instagram (IG) platforms. We observe minimal edge overlap between these networks, indicating users are exhibiting different behaviors and interaction patterns between platforms. We then compare predictive performance metrics across various node attribute prediction tasks for an ads click prediction task on Facebook and for a publicly available dataset from the Open Graph Benchmark. We adapt an existing node attribute prediction method for binary prediction, LINK-Naive Bayes, to account for both edge direction and weights on single-layer networks. We observe meaningful predictive performance gains when incorporating edge direction and weight. We then introduce an approach called MultiLayerLINK-NaiveBayes that can combine multiple network layers during training and observe superior performance over the single-layer results. Ultimately, whether edge direction, edge weights, and multi-layers are practically useful will depend on the particular setting. Our approach enables practitioners to quickly combine multiple layers and additional edge information such as direction or weight.

WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks

“…These researches believe the heterogeneous network is a more appropriate graph modeling paradigm to express the gigantic complex relationships and multityped entities in the real world. The researchers from both academia and industry have applied HNs in a wide range of data mining tasks, such as classification [1], [2], clustering [3], ranking, recommendation [4], similarity search [5] and prediction and so on.…”

“…• The Weighted-L1: |u − v| • Weighted-L2: |u − v| 2 The Area Under Curve(AUC) scores for link prediction are summarized in Table 8. From the results, we can see that WMGCN-1st outperforms most of the baselines scores.…”

A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

“…This paper is an extension of our previous conference paper "CHIN: Classification with META-PATH in Heterogeneous Information Networks [9]". We have significantly improved our previous work and presented new meta graphs based classification of heterogeneous information networks algorithms.…”

“…In the real world, there exist lots of various entities, and together with their inter-relationships, they can be represented as information networks [1], such as Bibliographic Information Networks [2], Wikipedia [3], and Facebook. Existing studies [4][5][6][7][8][9] mainly focus on representing and analyzing such systems using homogeneous information networks, which composed of one type of nodes and edges. For example, Figure 1a illustrates a co-author homogeneous network consisting of "authors" as nodes and "publishing" relationships as links.…”