mlsanityData to 14 Oct 2026

Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning

arXiv 2407.20648 ↗First posted 30 Jul 2024cs.LG cs.AIcited by 2

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Abstract (arXiv)

Recent advancements in graph neural networks (GNNs) and heterogeneous GNNs (HGNNs) have advanced node embeddings and relationship learning for various tasks. However, existing methods often rely on domain-specific predefined meta-paths, which are coarse-grained and focus solely on aspects like node type, limiting their ability to capture complex interactions. We introduce MF2Vec, a model that uses multi-faceted (fine-grained) paths instead of predefined meta-paths. MF2Vec extracts paths via random walks and generates multi-faceted vectors, ignoring predefined schemas. This method learns diverse aspects of nodes and their relationships, constructs a homogeneous network, and creates node embeddings for classification, link prediction, and clustering. Extensive experiments show that MF2Vec outperforms existing methods, offering a more flexible and comprehensive framework for analyzing complex networks. The code is available at https://anonymous.4open.science/r/MF2Vec-6ABC.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 30 Jul 2024.

Builds on

24 of 35 references.

References · 24

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12 more
  1. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  2. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  3. 2021Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learningcited by 130
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  6. 2019Is a Single Vector Enough? Exploring Node Polysemy for Network Embeddingcited by 29
  7. 2019Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contextscited by 19
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  12. 2020GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Networkcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priorscited by 10
  2. 2026Aspect-Aware MOOC Recommendation in a Heterogeneous Networkcited by 0