FOBE and HOBE: First- and High-Order Bipartite Embeddings

2019-05-27 · Justin Sybrandt, Ilya Safro

Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery. Machine learning methods utilized in these applications would be better served with specialized embedding techniques. We propose two embeddings for bipartite graphs that decompose edges into sets of indirect relationships between node neighborhoods. When sampling higher-order relationships, we reinforce similarities through algebraic distance on graphs. We also introduce ensemble embeddings to combine both into a "best of both worlds" embedding. The proposed methods are evaluated on link prediction and recommendation tasks and compared with other state-of-the-art embeddings. While being all highly beneficial in applications, we demonstrate that none of the considered embeddings is clearly superior (in contrast to what is claimed in many papers), and discuss the trade offs present among them. Reproducibility: Our code, data sets, and results are all publicly available online at: http://sybrandt.com/2020/fobe_hobe.

📄 PDF Abstract BibTeX arXiv:1905.10953

Code (0)

등록된 구현이 없습니다.

Tasks

Data VisualizationDrug Discovery Link Prediction Recommendation Systems Vocal Bursts Intensity Prediction

PhoBERT: Pre-trained language models for Vietnamese

2020-03-02 · Findings of the Association for Computational Linguistics 2020 · Dat Quoc Nguyen, Anh Tuan Nguyen

We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms t…

Dependency Parsing named-entity-recognition Named Entity Recognition Named Entity Recognition (NER)+4

InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective

2020-10-05 · ICLR 2021 1 · Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan 외

Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies, however, show that such BERT-based models are vulnerable facing the threats of textual…

Natural Language Inference Question Answering Representation Learning

InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping

2021-06-13 · Runshi Liu, Pengda Qin, Yuhong Li, Weigao Wen 외

E-commerce companies have to face abnormal sellers who sell potentially-risky products. Typically, the risk can be identified by jointly considering product content (e.g., title and image) and seller behavior. This work …

feature selection Management Representation Learning

HakushoBench: A Japanese Chart and Table VQA Benchmark from Governmental White Papers

2026-05-31 · Issa Sugiura, Shuhei Kurita, Yusuke Oda, Naoaki Okazaki arxiv

Understanding chart and table images is essential for applying vision-language models (VLMs) to real-world document understanding. While English benchmarks have advanced rapidly, non-English counterparts remain scarce, l…

Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs

2020-02-21 · Nate Veldt, Anthony Wirth, David F. Gleich

Motivated by applications in community detection and dense subgraph discovery, we consider new clustering objectives in hypergraphs and bipartite graphs. These objectives are parameterized by one or more resolution param…

Clustering Community Detection Graph Clustering