Advances in Graph Representation Learning

The field of graph representation learning is rapidly evolving, with a focus on developing innovative methods to capture complex relationships and structural properties of graphs. Recent research has explored the use of hypergraphs, multilevel graphs, and topology-aware vision transformers to model higher-order interactions and improve graph representation learning. Notable advancements include the development of scalable and flexible frameworks for learning node embeddings, graph-level clustering, and node classification. These advancements have significant implications for various applications, including music recommendation, citation networks, and drug discovery.

Noteworthy papers include:

  • Lib2Vec, which proposes a novel self-supervised framework for learning meaningful vector representations of library cells.
  • MARIOH, which introduces a supervised approach for reconstructing the original hypergraph from its projected graph by leveraging edge multiplicity.
  • HGFormer, which presents a topology-aware vision transformer that integrates hypergraph topology as perceptual indications to guide the aggregation of global and unbiased information.
  • SIGNNet, which proposes a novel framework that capitalizes on local and global structural information to effectively capture fine-grained relationships and broader contextual patterns within the graph structure.

Sources

Learning Library Cell Representations in Vector Space

Space of Data through the Lens of Multilevel Graph

Node Embeddings via Neighbor Embeddings

MARIOH: Multiplicity-Aware Hypergraph Reconstruction

ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities

Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering

Efficient Computation of Hyper-triangles on Hypergraphs

HGFormer: Topology-Aware Vision Transformer with HyperGraph Learning

Graphs are everywhere -- Psst! In Music Recommendation too

A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification

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