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20162025
most citedHICF: Hyperbolic Informative Collaborative Filtering

57 citations · 100 across the 20 of their papers we have counts for

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Showing 2023Show all

11 papers · 1 filter

cs.DB2023

An Augmented Index-based Efficient Community Search for Large Directed Graphs

Yankai Chen, Jie Zhang, Yixiang Fang +2

Given a graph G and a query vertex q, the topic of community search (CS), aiming to retrieve a dense subgraph of G containing q, has gained much attention. Most existing works focu…

cs.CV2023

Generalized Category Discovery with Clustering Assignment Consistency

Xiangli Yang, Xinglin Pan, Irwin King +1

Generalized category discovery (GCD) is a recently proposed open-world task. Given a set of images consisting of labeled and unlabeled instances, the goal of GCD is to automaticall…

cs.CL20231 cited

Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue

Hongru Wang, Minda Hu, Yang Deng +7

Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue sys…

physics.chem-ph20231 cited

Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction

Ziqiao Meng, Peilin Zhao, Yang Yu +1

Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of elect…

cs.CL2023

Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis

Xuming Hu, Zhijiang Guo, Zhiyang Teng +2

Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair. Existing retrieval-…

cs.LG20231 cited

FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks

Xinyu Fu, Irwin King

Heterogeneous graph neural networks (HGNNs) can learn from typed and relational graph data more effectively than conventional GNNs. With larger parameter spaces, HGNNs may require…