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- Pfp535
Through empirical study on a large dataset of 802,939 Web pages and 1,021,107 tags, it is demonstrated that PFP can achieve virtually linear speedup and to be promising for supporting query recommendation for search engines.
- Combating Fake News509
This survey describes the modern-day problem of fake news and, in particular, highlights the technical challenges associated with it and comprehensively compile and summarize characteristic features of available datasets.
- Session-Based Social Recommendation via Dynamic Graph Attention Networks488
This work proposes a recommender system for online communities based on a dynamic-graph-attention neural network, which dynamically infers the influencers based on users' current interests and can be efficiently fit on large-scale data.
- Topic sentiment analysis in twitter484
This study focuses on hashtag-level sentiment classification, which aims to automatically generate the overall sentiment polarity for a given hashtag in a certain time period, and proposes a novel graph model and three approximate collective classification algorithms for inference.
- Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis284
This paper presents theorems elucidating the posterior contraction rates of the topics as the amount of data increases, and a thorough supporting empirical study using synthetic and real data sets, including news and web-based articles and tweet messages.
- We know what @you #tag266
This work proposes comprehensive measures to quantify the major factors of how a user selects content tags as well as joins communities, and proves the effectiveness of the dual role, where both the content measures and the community measures significantly correlate to hashtag adoption on Twitter.
- DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation188
This paper uses meta relations to decompose high-order connectivity between node pairs and proposes a disentangled embedding propagation layer which can iteratively identify the major aspect of meta relations in a heterogeneous information network.
- An Attention-based Collaboration Framework for Multi-View Network Representation Learning181
Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.
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- A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook142
This article introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks, and delves into the potential future directions behind the emergence of reasoning abilities within foundation models.
- An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems112
Experimental results show that a novel ensemble of retrieval-based and generation-based conversation system outperforms each single module by a large margin.
- A Diffusion Model for POI Recommendation107
Inspired by the wide application of diffusion algorithm in sampling from distributions, Diff-POI encodes the user’s visiting sequence and spatial character with two tailor-designed graph encoding modules, followed by a diffusion-based sampling strategy to explore the user’s spatial visiting trends.
- DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation104
This paper proposes DisenPOI, a novel Disentangled dual-graph framework for POI recommendation, which jointly utilizes sequential and geographical relationships on two separate graphs and disentangles the two influences with self-supervision.
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- Text Classification with Heterogeneous Information Network Kernels98
A novel text as network classification framework, which introduces a structured and typed heterogeneous information networks (HINs) representation of texts, and a meta-path based approach to link texts that outperforms the state-of-the-art methods and other HIN-kernels.
- Few-shot Molecular Property Prediction via Hierarchically Structured Learning on Relation Graphs84
A novel framework called Hierarchically Structured Learning on Relation Graphs (HSL-RG) for molecular property prediction, which explores the structural semantics of a molecule from both global-level and local-level granularities, and proposes a task-adaptive meta-learning algorithm to provide meta knowledge customization for different tasks in few-shot scenarios.
- Learning Graph ODE for Continuous-Time Sequential Recommendation78
A novel ordinary differential equation based GNN is introduced to implicitly model the temporal evolution of the user-item interaction graph and an attention-based GNN is proposed to explicitly incorporate collaborative attention to interaction signals when the interaction graph evolves over time.
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- Self-supervised Graph-level Representation Learning with Adversarial Contrastive Learning71
This work proposes a Graph Adversarial Contrastive Learning (GraphACL) scheme that learns a bank of negative samples for effective self-supervised whole-graph representation learning and optimize the parameters of the graph encoding branch and adversarial generation branch alternately.
- Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting71
This paper proposes a novel model named Dynamic Hypergraph Structure Learning (DyHSL) for traffic flow prediction, which extracts hypergraph structural information to model dynamics in the traffic networks, and updates each node representation by aggregating messages from its associated hyperedges.
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- Unsupervised graph-level representation learning with hierarchical contrasts66
An unsupervised graph-level representation learning framework named Hierarchical Graph Contrastive Learning (HGCL), which investigates the hierarchical structural semantics of a graph at both node and graph levels and significantly outperforms a broad range of state-of-the-art baselines.
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- An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing64
The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail and a prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach.
- SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis62
A new Supervised User-Item based Topic model, called SUIT model, is proposed, which can simultaneously utilize the textual topic and latent user-item factors and shows significant improvement compared with supervised topic models and collaborative filtering methods.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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