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Found 11 papers, 5 papers with code
SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance.
Less is More: Reweighting Important Spectral Graph Features for Recommendation
To unveil the effectiveness of GCNs for recommendation, we first analyze them in a spectral perspective and discover two important findings: (1) only a small portion of spectral graph features that emphasize the neighborhood smoothness and difference contribute to the recommendation accuracy, whereas most graph information can be considered as noise that even reduces the performance, and (2) repetition of the neighborhood aggregation emphasizes smoothed features and filters out noise information in an ineffective way.
Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries
In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS).
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data.
Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture
Users participate in online discussion forums to learn from others and share their knowledge with the community.
Neural Multi-Task Learning for Citation Function and Provenance
Citation function and provenance are two cornerstone tasks in citation analysis.
Treatment Side Effect Prediction from Online User-generated Content
With Health 2. 0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities.
Identifying Emergent Research Trends by Key Authors and Phrases
Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers.
Abstractive Meeting Summarization UsingDependency Graph Fusion
Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries.
Generating Abstractive Summaries from Meeting Transcripts
The most informative and well-formed sub-graph obtained by integer linear programming (ILP) is selected to generate a one-sentence summary for each topic segment.
Multi-document abstractive summarization using ILP based multi-sentence compression
The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences.
