{"list":"other","total":52,"rows":[{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2407.21783","title":"The Llama 3 Herd of Models","created":"2024-07-31","cited_by_count":14715},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"2005.11401","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","created":"2020-05-22","cited_by_count":9297},{"id":"1908.10084","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","created":"2019-08-27","cited_by_count":9170},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"2508.10925","title":"gpt-oss-120b & gpt-oss-20b Model Card","created":"2025-08-08","cited_by_count":1386},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"2003.00982","title":"Benchmarking Graph Neural Networks","created":"2020-03-02","cited_by_count":680},{"id":"2003.02320","title":"Knowledge Graphs","created":"2020-03-04","cited_by_count":621},{"id":"2006.10637","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","created":"2020-06-18","cited_by_count":530},{"id":"1904.03751","title":"DeepGCNs: Can GCNs Go as Deep as CNNs?","created":"2019-04-07","cited_by_count":527},{"id":"2002.07962","title":"Inductive Representation Learning on Temporal Graphs","created":"2020-02-19","cited_by_count":462},{"id":"1908.01207","title":"Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks","created":"2019-08-03","cited_by_count":448},{"id":"1902.10191","title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs","created":"2019-02-26","cited_by_count":447},{"id":"1612.07659","title":"Structured Sequence Modeling with Graph Convolutional Recurrent Networks","created":"2016-12-22","cited_by_count":275},{"id":"2206.09426","title":"ADBench: Anomaly Detection Benchmark","created":"2022-06-19","cited_by_count":232},{"id":"2401.04398","title":"Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding","created":"2024-01-09","cited_by_count":188},{"id":"2505.00212","title":"Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems","created":"2025-04-30","cited_by_count":185},{"id":"2103.00113","title":"Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning","created":"2021-02-27","cited_by_count":177},{"id":"2402.17944","title":"Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey","created":"2024-02-27","cited_by_count":159},{"id":"2307.01026","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","created":"2023-07-03","cited_by_count":138},{"id":"2208.07239","title":"ROLAND: Graph Learning Framework for Dynamic Graphs","created":"2022-08-15","cited_by_count":132},{"id":"2103.04475","title":"LogBERT: Log Anomaly Detection via BERT","created":"2021-03-07","cited_by_count":118},{"id":"2301.10140","title":"The Semantic Scholar Open Data Platform","created":"2023-01-24","cited_by_count":118},{"id":"1905.00075","title":"On the Use of ArXiv as a Dataset","created":"2019-04-30","cited_by_count":103},{"id":"2409.17515","title":"From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection","created":"2024-09-26","cited_by_count":98},{"id":"2306.11025","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","created":"2023-06-19","cited_by_count":77},{"id":"2403.19318","title":"TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios","created":"2024-03-28","cited_by_count":77},{"id":"2406.12031","title":"Large Scale Transfer Learning for Tabular Data via Language Modeling","created":"2024-06-17","cited_by_count":76},{"id":"2008.06448","title":"Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics","created":"2020-08-14","cited_by_count":70},{"id":"2302.01018","title":"Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities","created":"2023-02-02","cited_by_count":61},{"id":"2305.16646","title":"Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning","created":"2023-05-26","cited_by_count":45},{"id":"2407.01231","title":"MIRAI: Evaluating LLM Agents for Event Forecasting","created":"2024-07-01","cited_by_count":41},{"id":"2312.16702","title":"Rethinking Tabular Data Understanding with Large Language Models","created":"2023-12-27","cited_by_count":36},{"id":"2309.01189","title":"LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection","created":"2023-09-03","cited_by_count":35},{"id":"2111.09564","title":"LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model","created":"2021-11-18","cited_by_count":30},{"id":"2310.19324","title":"TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery","created":"2023-10-30","cited_by_count":27},{"id":"2406.12072","title":"DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs","created":"2024-06-17","cited_by_count":27},{"id":"2408.13727","title":"LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models","created":"2024-08-25","cited_by_count":20},{"id":"2311.05261","title":"RAGLog: Log Anomaly Detection using Retrieval Augmented Generation","created":"2023-11-09","cited_by_count":19},{"id":"2403.05026","title":"Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts","created":"2024-03-08","cited_by_count":19},{"id":"2406.00134","title":"Anomaly Detection in Dynamic Graphs: A Comprehensive Survey","created":"2024-05-31","cited_by_count":19},{"id":"2205.13426","title":"AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks","created":"2022-05-26","cited_by_count":18},{"id":"2502.00677","title":"LLM-based event log analysis techniques: A survey","created":"2025-02-02","cited_by_count":16},{"id":"2507.10539","title":"Graph World Model","created":"2025-07-14","cited_by_count":16},{"id":"2508.00217","title":"Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges","created":"2025-07-31","cited_by_count":10},{"id":"2407.03689","title":"Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models","created":"2024-07-04","cited_by_count":9},{"id":"2412.03930","title":"GuARD: Effective Anomaly Detection through a Text-Rich and Graph-Informed Language Model","created":"2024-12-05","cited_by_count":6},{"id":"2309.05953","title":"GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection","created":"2023-09-12","cited_by_count":3}],"next_cursor":null}