Graph Ranking
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Benchmarks
Most implemented
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs
Personalized Ranking on Cascading Behavior Graphs for Accurate Multi-Behavior Recommendation
GUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT
Ranking Structured Objects with Graph Neural Networks
Papers
Target-Oriented Pretraining Data Selection via Neuron-Activated Graph
Everyday tasks come with a target, and pretraining models around this target is what turns them into experts. In this paper, we study target-oriented language model (LM) pretraining by introducing Neuron-Activated Graph …
Graph RankingPersonalized Ranking on Cascading Behavior Graphs for Accurate Multi-Behavior Recommendation
Multi-behavior recommendation predicts items a user may purchase by analyzing diverse behaviors like viewing, adding to a cart, and purchasing. Existing methods fall into two categories: representation learning and graph…
Graph RankingRepresentation LearningAdvanced Academic Team Worker Recommendation Models
Collaborator recommendation is an important task in academic domain. Most of the existing approaches have the assumption that the recommendation system only need to recommend a specific researcher for the task. However, …
Graph RankingGraph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method
Graph contrastive learning (GCL) has emerged as a representative graph self-supervised method, achieving significant success. The currently prevalent optimization objective for GCL is InfoNCE. Typically, it employs augme…
Contrastive LearningGraph LearningGraph RankingToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds. Most VS methods to date have…
Drug DiscoveryGraph RankingTripletGUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT
Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-based methods, vertex and edge weights are usually created by calcul…
Document SummarizationExtractive Document SummarizationExtractive Text SummarizationGraph Ranking+5