{"list":"other","total":79,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"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":"2001.08361","title":"Scaling Laws for Neural Language Models","created":"2020-01-23","cited_by_count":6586},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"2410.21276","title":"GPT-4o System Card","created":"2024-10-25","cited_by_count":4588},{"id":"2210.08402","title":"LAION-5B: An open large-scale dataset for training next generation image-text models","created":"2022-10-16","cited_by_count":4152},{"id":"1701.06538","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","created":"2017-01-23","cited_by_count":3509},{"id":"2310.06825","title":"Mistral 7B","created":"2023-10-10","cited_by_count":3164},{"id":"2303.15343","title":"Sigmoid Loss for Language Image Pre-Training","created":"2023-03-27","cited_by_count":3109},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1910.03771","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","created":"2019-10-09","cited_by_count":2218},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1809.10341","title":"Deep Graph Infomax","created":"2018-09-27","cited_by_count":1106},{"id":"2004.09297","title":"MPNet: Masked and Permuted Pre-training for Language Understanding","created":"2020-04-20","cited_by_count":981},{"id":"2305.14233","title":"Enhancing Chat Language Models by Scaling High-quality Instructional Conversations","created":"2023-05-23","cited_by_count":839},{"id":"2308.03281","title":"Towards General Text Embeddings with Multi-stage Contrastive Learning","created":"2023-08-07","cited_by_count":723},{"id":"2210.07316","title":"MTEB: Massive Text Embedding Benchmark","created":"2022-10-13","cited_by_count":640},{"id":"2212.10403","title":"Towards Reasoning in Large Language Models: A Survey","created":"2022-12-20","cited_by_count":596},{"id":"2205.10803","title":"GraphMAE: Self-Supervised Masked Graph Autoencoders","created":"2022-05-22","cited_by_count":448},{"id":"2402.07927","title":"A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications","created":"2024-02-05","cited_by_count":431},{"id":"2404.05961","title":"LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders","created":"2024-04-09","cited_by_count":404},{"id":"2103.09430","title":"OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs","created":"2021-03-17","cited_by_count":329},{"id":"2103.00111","title":"Graph Self-Supervised Learning: A Survey","created":"2021-02-27","cited_by_count":289},{"id":"2307.03393","title":"Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs","created":"2023-07-07","cited_by_count":274},{"id":"2007.02901","title":"Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks","created":"2020-07-06","cited_by_count":260},{"id":"2310.00149","title":"One for All: Towards Training One Graph Model for All Classification Tasks","created":"2023-09-29","cited_by_count":243},{"id":"2310.13023","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","created":"2023-10-19","cited_by_count":226},{"id":"2402.08170","title":"LLaGA: Large Language and Graph Assistant","created":"2024-02-13","cited_by_count":181},{"id":"2210.14709","title":"Learning on Large-scale Text-attributed Graphs via Variational Inference","created":"2022-10-26","cited_by_count":155},{"id":"2105.02605","title":"GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph","created":"2021-05-06","cited_by_count":143},{"id":"2111.00064","title":"Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction","created":"2021-10-29","cited_by_count":138},{"id":"2305.19523","title":"Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning","created":"2023-05-31","cited_by_count":133},{"id":"2402.05862","title":"Let Your Graph Do the Talking: Encoding Structured Data for LLMs","created":"2024-02-08","cited_by_count":118},{"id":"2402.09834","title":"All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining","created":"2024-02-15","cited_by_count":86},{"id":"2402.11235","title":"ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs","created":"2024-02-17","cited_by_count":85},{"id":"2407.15831","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","created":"2024-07-22","cited_by_count":84},{"id":"2409.15700","title":"Making Text Embedders Few-Shot Learners","created":"2024-09-24","cited_by_count":79},{"id":"1301.6725","title":"Loopy Belief Propagation for Approximate Inference: An Empirical Study","created":"2013-01-23","cited_by_count":76},{"id":"2308.02565","title":"SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning","created":"2023-08-03","cited_by_count":73},{"id":"2310.04668","title":"Label-free Node Classification on Graphs with Large Language Models (LLMS)","created":"2023-10-07","cited_by_count":72},{"id":"2402.12984","title":"Can GNN be Good Adapter for LLMs?","created":"2024-02-20","cited_by_count":71},{"id":"2306.13089","title":"GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning","created":"2023-05-28","cited_by_count":70},{"id":"2402.07197","title":"GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks","created":"2024-02-11","cited_by_count":63},{"id":"2408.14512","title":"LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings","created":"2024-08-25","cited_by_count":61},{"id":"2410.10329","title":"GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs","created":"2024-10-14","cited_by_count":58},{"id":"2407.09709","title":"GOFA: A Generative One-For-All Model for Joint Graph Language Modeling","created":"2024-07-12","cited_by_count":57},{"id":"1705.05085","title":"Active Learning for Graph Embedding","created":"2017-05-15","cited_by_count":54},{"id":"2101.06323","title":"TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search","created":"2021-01-15","cited_by_count":54},{"id":"2408.10700","title":"AnyGraph: Graph Foundation Model in the Wild","created":"2024-08-20","cited_by_count":53},{"id":"2406.15992","title":"Can LLM Graph Reasoning Generalize beyond Pattern Memorization?","created":"2024-06-23","cited_by_count":47},{"id":"2406.10727","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","created":"2024-06-15","cited_by_count":44},{"id":"2104.12080","title":"AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search","created":"2021-04-25","cited_by_count":38},{"id":"2310.18152","title":"Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs","created":"2023-10-27","cited_by_count":36},{"id":"2407.07457","title":"GLBench: A Comprehensive Benchmark for Graph with Large Language Models","created":"2024-07-10","cited_by_count":36},{"id":"2402.02130","title":"GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning","created":"2024-02-03","cited_by_count":33},{"id":"2404.14928","title":"Graph Machine Learning in the Era of Large Language Models (LLMs)","created":"2024-04-23","cited_by_count":33},{"id":"2410.05298","title":"How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension","created":"2024-10-04","cited_by_count":30},{"id":"2110.14854","title":"RIM: Reliable Influence-based Active Learning on Graphs","created":"2021-10-28","cited_by_count":23},{"id":"2402.12022","title":"Distilling Large Language Models for Text-Attributed Graph Learning","created":"2024-02-19","cited_by_count":20},{"id":"2207.11311","title":"Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective","created":"2022-07-22","cited_by_count":19},{"id":"2409.03258","title":"GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding","created":"2024-09-05","cited_by_count":18},{"id":"2406.02953","title":"GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment","created":"2024-06-05","cited_by_count":15},{"id":"2406.12052","title":"UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding","created":"2024-06-17","cited_by_count":14},{"id":"2201.09391","title":"Partition-Based Active Learning for Graph Neural Networks","created":"2022-01-23","cited_by_count":12},{"id":"2412.00315","title":"One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs","created":"2024-11-30","cited_by_count":12},{"id":"2402.03720","title":"Similarity-based Neighbor Selection for Graph LLMs","created":"2024-02-06","cited_by_count":10},{"id":"2406.10310","title":"TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs","created":"2024-06-14","cited_by_count":9},{"id":"2406.14683","title":"TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models","created":"2024-06-20","cited_by_count":9},{"id":"2412.06849","title":"GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model","created":"2024-12-08","cited_by_count":8},{"id":"2410.11370","title":"Enhance Graph Alignment for Large Language Models","created":"2024-10-15","cited_by_count":7},{"id":"2409.20053","title":"GUNDAM: Aligning Large Language Models with Graph Understanding","created":"2024-09-30","cited_by_count":5},{"id":"2407.12860","title":"STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs","created":"2024-07-10","cited_by_count":3},{"id":"2407.13989","title":"Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models","created":"2024-07-19","cited_by_count":3}],"next_cursor":null}