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- Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks863
A novel bi-directional graph model is proposed, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics of rumors by operating on both top-down and bottom-up propagation of rumors.
- Graph Representation Learning via Graphical Mutual Information Maximization706
An unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder is developed, which outperforms state-of-the-art unsuper supervised counterparts, and even sometimes exceeds the performance of supervised ones.
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- Graph Convolutional Networks for Temporal Action Localization549
This paper builds an action proposal graph, where each proposal is represented as a node and their relations between two proposals as an edge and applies the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
- Progressive Feature Alignment for Unsupervised Domain Adaptation490
The Progressive Feature Alignment Network (PFAN) is proposed to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain.
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- Multicolor Fluorescent Semiconducting Polymer Dots with Narrow Emissions and High Brightness215
Fluorescence imaging and flow cytometry experiments indicate the narrow emissions from these bright Pdots are promising for multiplexed biological detections.
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- A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models180
This paper investigates the theoretical connections between graph signal processing and graph embedding models in a principled way and forms a generalized adversarial attacker, GF-Attack, which is constructed by the graph filter and feature matrix and validated on several benchmark datasets.
- Semi-Supervised Graph Classification: A Hierarchical Graph Perspective172
This work proposes a novel supervised, self-attentive graph embedding method called SAGE, which embeds graph instances of arbitrary size into fixed-length vectors and demonstrates that SEAL-C/AI not only outperform competing methods by a significant margin in terms of accuracy/Macro-F1, but also generate meaningful interpretations of the learned representations.
- Pose-Robust Face Recognition via Deep Residual Equivariant Mapping169
A novel Deep Residual EquivAriant Mapping (DREAM) block is formulated, which is capable of adaptively adding residuals to the input deep representation to transform a profile face representation to a canonical pose that simplifies recognition.
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- Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning155
From a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators.
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- A survey of geometric graph neural networks: data structures, models and applications97
This paper formalizes geometric graph as the data structure, on top of which it provides a unified view of existing models from the geometric message passing perspective, and summarizes the applications as well as the related datasets to facilitate later research for methodology development and experimental evaluation.
- Graph Convolutional Module for Temporal Action Localization in Videos93
It is claimed that the relations between action units play an important role in action localization, and a more powerful action detector should not only capture the local content of each action unit but also allow a wider field of view on the context related to it to enhance action localization.
- Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising91
Deep Neural Auctions are designed to enable end-to-end auction learning by proposing a differentiable model to relax the discrete sorting operation, a key component in auctions.
- Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media88
A fake news detection model named Post-User Interaction Network (PSIN) is proposed, which adopts a divide-and-conquer strategy to model the post-post, user-user and post-user interactions in social context effectively while maintaining their intrinsic characteristics.
- DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations81
DrugOOD offers an automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise level annotations, and rigorous benchmarking of SOTA OOD algorithms, as opposed to only providing fixed datasets.
- Cross-dependent graph neural networks for molecular property prediction81
This work explores the multi-view modeling with graph neural network to form a novel paralleled framework which considers both atoms and bonds equally important when learning molecular representations, and proposes a cross-dependent message passing scheme to enhance information communication of different views.
- Recognizing Predictive Substructures With Subgraph Information Bottleneck81
A novel subgraph information bottleneck (SIB) framework to recognize a predictive yet compressed subgraph to get rid of the noise and redundancy and obtain the interpretable part of the graph and theoretically prove the error bound of the estimation scheme for mutual information and the noise-invariant nature of IB-subgraph.
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- Query driven-graph neural networks for community search66
Graph Neural Network (GNN) models for both CS and ACS problems are proposed, i.e., Query Driven-GNN (QD- GNN) and Attributed Query Dri driven GNN (AQD -GNN), which combine the local query-dependent structure and global graph embedding and model vertex attributes as a bipartite graph.
- On Self-Distilling Graph Neural Network65
This paper proposes the first teacher-free knowledge distillation method for GNNs, termed GNN Self-Distillation (GNN-SD), that serves as a drop-in replacement of the standard training process and proposes the adaptive discrepancy retaining (ADR) regularizer to empower the transferability of knowledge that maintains high neighborhood discrepancy across GNN layers.
- Computing Graph Edit Distance via Neural Graph Matching62
A novel deep learning framework is proposed that solves the GED problem in a two-step manner: the proposed graph neural network GEDGNN is in charge of predicting the G ED value and a matching matrix; and a post-processing algorithm based on k -best matching is used to derive possible node matchings from the matching matrix generated by G EDGNN.
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- A Learned Sketch for Subgraph Counting57
An Active Learned Sketch for Subgraph Counting (\ALSS) is proposed with two main components: a sketch learned (ŁSS) and an active learner ( \AL) to perform model updates based on new arrival test query graphs.
- TATC54
This work presents a novel solution named time-aware TICC and CNN (TATC), for predicting AD from actigraphy data, which not only performs accurate prediction of AD risk, but also generates meaningful interpretation of daily behavior pattern of subjects.
- Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer52
Neighbor-Interaction based CTR prediction (NI-CTR) is proposed, which considers this task under a Heterogeneous Information Network (HIN) setting and outperforms state-of-the-art CTR models significantly, and is deployed on the WeChat Official Account Platform with billions of users.
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- Blood–brain barrier penetration prediction enhanced by uncertainty estimation48
The results exhibit that, despite the comparable performance on BBBp prediction between graph neural networks-based deep learning models and conventional physicochemical-based machine learning models, the GROVER-BBBp model shows greatly improvement when using uncertainty estimations.
- Deep Graph Learning46
This tutorial aims to provide a comprehensive introduction to deep graph learning and introduces the applications of DGL towards various domains, including but not limited to drug discovery, computer vision, medical image analysis, social network analysis, natural language processing and recommendation.
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- Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding41
This paper proposes an unsupervised alignment framework named Large-Scale Network Alignment (LSNA) to integrate the network information and reduce the requirement on user profile, and proposes a network disassembling strategy to divide the costly large-scale network alignment problem into multiple executable sub-problems.
- A Monte Carlo algorithm for cold start recommendation41
This work defines a random walk on a bipartite graph of users and items to simulate the preference propagation among users, and proposes a Monte Carlo algorithm to estimate the similarity between different users.
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- A Novel Scalarized Scaffold Hopping Algorithm with Graph-Based Variational Autoencoder for Discovery of JAK1 Inhibitors38
A graph-based Variational Autoencoder with Gaussian Mixture hidden space with deep learning approach for controllable magnitude of scaffold hopping in generative chemistry can effectively and accurately generate molecules from a given reference compound, with excellent scaffold novelty against known molecules in the literature or patents.
- Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation36
This work comprehensively evaluates the robustness of existing UDA methods and proposes a robust UDA approach that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space and is resistant to adversarial attacks.
- Hierarchical Graph Capsule Network36
This paper innovatively proposes hierarchical graph capsule network (HGCN) that can jointly learn node embeddings and extract graph hierarchies and demonstrates the effectiveness of HGCN and the contribution of each component.
- Semi-Supervised Hierarchical Graph Classification35
This work designs a novel semi-supervised solution to the node classification problem in the hierarchical graph where a “node” is a graph instance, and proposes the Hierarchical Graph Mutual Information (HGMI) and presents a way to compute HGMI with theoretical guarantee.
- A Model-Free Approach to Infer the Diffusion Network from Event Cascade35
The proposed NPDC algorithm infers the diffusion network according to the statistical difference of the infection time intervals between nodes connected with diffusion edges versus those with no diffusion edges, and is a model-free approach since it does not define any transmission models between nodes in advance.
- A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection33
This survey provides a comprehensive review of recent leading approaches in the TwGL field from three dimensions, namely, reliability, explainability, and privacy protection, and gives a general categorization for existing work and review typical work for each category.
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- Lingshu: Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning17
This work proposes a comprehensive data-curation procedure that efficiently acquires rich medical knowledge data not only from medical imaging but also from extensive medical texts and general domain data and develops a medical-specialized MLLM, Lingshu-Med, which consistently outperforms existing multimodal baselines in most tasks.
- De novo design of epitope-specific antibodies via a structure-driven computational workflow17
The tFold System overcomes key limitations of existing methods by enabling rapid, high-throughput antibody discovery against user-defined epitopes, and designs epitope-specific antibodies, with their nanomolar affinities experimentally confirmed.
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- Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation10
A customized encryption scheme named orthogonal matrix-based mask mechanism (O3M), a tailored component for contextual bandits by carefully exploiting their shared structure, can ensure privacy protection while avoiding expensive conventional cryptographic techniques.
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- Geometric Graph Learning for Protein Mutation Effect Prediction3
A new graph learning framework, Hierarchical Graph Invariant Network (HGIN), a fine-grained and data-efficient graph neural encoder for encoding protein structures and predicting the mutation effect on protein properties, which outperforms 9 state-of-the-art approaches on 3 protein datasets.
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- A Unified Random Walk, Its Induced Laplacians and Spectral Convolutions for Deep Hypergraph Learning2
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- Accurate protein-protein interactions modeling through physics-informed geometric invariant learning1
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- One Path to Model Them All: Learnable-Time Flow Matching for Crystal Structure and Energy Prediction–
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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 2026-10-13. 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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