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- Graph Self-Supervised Learning: A Survey786
A unified framework is constructed that mathematically formalizes the paradigm of graph SSL and describes the applications of graphSSL across various research fields and summarize the commonly used datasets, evaluation benchmark, performance comparison and open-source codes ofgraph SSL.
- Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning537
This article presents a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation), which fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.
- A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT411
This survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, cross-domain learning ability, and user-friendly interactive ability for artificial general intelligence.
- Graph Neural Networks for Graphs With Heterophily: A Survey343
A systematic taxonomy that governs existing heterophilic GNN models for heterophilic graphs is proposed, aiming to facilitate the development of more effective GNNs across a spectrum of practical applications and learning tasks in the graph research community.
- Towards Unsupervised Deep Graph Structure Learning299
A novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning is proposed, where the learned graph topology is optimized by data itself without any external guidance.
- Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection221
This paper proposes a novel method, Self-Supervised Learning for Graph Anomaly Detection (SL-GAD), which outperforms state-of-the-art methods by a large margin and employs two modules, generative attribute regression and multi-view contrastive learning for anomaly detection.
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- MMVU: Measuring Expert-Level Multi-Discipline Video Understanding167
This work introduces $\color{Blue}{\text{MMVU}}$, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding, and conducts an extensive evaluation of 36 frontier multimodal foundation models on $\color{Blue}{\text{MMVU}}$.
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- Emerging trends in federated learning: from model fusion to federated X learning159
Various learning algorithms to improve the vanilla federated averaging algorithm are explored and model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods are reviewed.
- On Learning to Summarize with Large Language Models as References146
This work uses LLMs as both oracle summary generators for standard supervised fine-tuning and oracle summary evaluators for efficient contrastive learning that leverages the LLMs’ supervision signals to investigate whether their performance can be substantially improved.
- Towards Self-Interpretable Graph-Level Anomaly Detection104
A Self-Interpretable Graph aNomaly dETection model (SIGNET for short) that detects anomalous graphs as well as generates informative explanations simultaneously and is able to not only measure the abnormality of each graph based on cross-view mutual information but also provide informative graph rationales by extracting bottleneck subgraphs from the input graph and its dual hypergraph in a self-supervised way.
- GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection96
A new graph contrastive learning framework GOOD-D is developed, aiming at detecting OOD graphs solely based on unlabeled ID data, and a comprehensive benchmark is built to compare the proposed approach with different state-of-the-art methods.
- Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts83
This work discovers a system prompt leakage vulnerability in GPT-4V and proposes a novel MLLM jailbreaking attack method termed SASP (Self-Adversarial Attack via System Prompt), demonstrating the important role of system prompts in jailbreaking.
- ARC: A Generalist Graph Anomaly Detector with In-Context Learning75
ARC is proposed, a generalist GAD approach that enables a ``one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly without the need for retraining or fine-tuning on the target dataset.
- Improving Large Language Model Fine-tuning for Solving Math Problems74
A fine-tuning recipe is designed that yields an 11.2% accuracy improvement over the few-shot performance of pre-trained PaLM 2-L model with majority voting and a multi-task sequential fine- Tuning, which integrates both solution generation and evaluation tasks together efficiently to enhance the LLM performance.
- Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation72
This work reframe MAS design as a conditional autoregressive graph generation task, where both the system composition and structure are designed jointly and proposes ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
- Learning Strong Graph Neural Networks with Weak Information70
D2PT is proposed, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
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- On Learning Text Style Transfer with Direct Rewards45
This paper uses semantic similarity metrics originally used for fine-tuning neural machine translation models to explicitly assess the preservation of content between system outputs and input texts to explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the style-transferred outputs.
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- RefSum: Refactoring Neural Summarization44
This work highlights several limitations of previous methods and presents a new framework Refactor that provides a unified view of text summarization and summaries combination and can be directly used by other researchers as an off-the-shelf tool to achieve further performance improvements.
- PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection42
This work introduces a simple method termed PREprocessing and Matching, which eliminates the necessity for message-passing propagation during training, and employs a simple contrastive loss, leading to considerable reductions in training time and memory usage.
- M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models38
M3SciQA, a multi-modal, multi-document scientific question answering benchmark designed for a more comprehensive evaluation of foundation models, indicates that current foundation models still significantly underperform compared to human experts in multi-modal information retrieval and in reasoning across multiple scientific documents.
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 2026-10-12. 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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