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Benchmarking the capabilities and limitations of large language models (LLMs) in graph-related tasks is becoming an increasingly popular and crucial area of research.
Local and global properties in networks of processors
Dana Angluin · 1980
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Network motifs: simple building blocks of complex networks
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
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Fast graph pattern matching
Jiefeng Cheng, Jeffrey Xu Yu, Bolin Ding, S Yu Philip, and Haixun Wang · 2008
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Mining significant graph patterns by leap search
Xifeng Yan, Hong Cheng, Jiawei Han, and Philip S Yu · 2008
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Christopher W Murray and David C Rees · 2009
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Ines Filipa Martins, Ana L Teixeira, Luis Pinheiro, and Andre O Falcao · 2012
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Extracting analyzing and visualizing triangle k-core motifs within networks
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Counting motifs in the human interactome
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On the power of the congested clique model
Andrew Drucker, Fabian Kuhn, and Rotem Oshman · 2014
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Grami: Frequent subgraph and pattern mining in a single large graph
Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis · 2014
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Deterministic subgraph detection in broadcast congest
Janne H Korhonen and Joel Rybicki · 2017
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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What graph neural networks cannot learn: depth vs width
Andreas Loukas · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
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Graph neural network for fraud detection via spatial-temporal attention
Dawei Cheng, Xiaoyang Wang, Ying Zhang, and Liqing Zhang · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Jiayan Guo, Lun Du, Hengyu Liu, Mengyu Zhou, Xinyi He, and Shi Han · 2023
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Molca: Molecular graph-language modeling with cross-modal projector and uni-modal adapter
Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, and Tat-Seng Chua · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, Yongfeng Zhang, et al · 2023
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Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Evaluating attribution for graph neural networks
Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang, Wesley Qian, Kevin McCloskey, Lucy Colwell, and Alexander Wiltschko · 2020
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On analyzing graphs with motif-paths
Xiaodong Li, Reynold Cheng, Kevin Chen-Chuan Chang, Caihua Shan, Chenhao Ma, and Hongtai Cao · 2021
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Ada-gnn: Adapting to local patterns for improving graph neural networks
Zihan Luo, Jianxun Lian, Hong Huang, Hai Jin, and Xing Xie · 2022
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Clare: A semi-supervised community detection algorithm
Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Caihua Shan, Yiheng Sun, Yangyong Zhu, and Philip S Yu · 2022
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A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests
Bohang Zhang, Guhao Feng, Yiheng Du, Di He, and Liwei Wang · 2023
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Graphtext: Graph reasoning in text space
Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang · 2023
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Large language models for scientific synthesis, inference and explanation
Yizhen Zheng, Huan Yee Koh, Jiaxin Ju, Anh TN Nguyen, Lauren T May, Geoffrey I Webb, and Shirui Pan · 2023
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Simulation of graph algorithms with looped transformers
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Towards revealing the mystery behind chain of thought: a theoretical perspective
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Can llms effectively leverage graph structural information through prompts, and why?
Jin Huang, Xingjian Zhang, Qiaozhu Mei, and Jiaqi Ma · 2024
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Chain of thought empowers transformers to solve inherently serial problems
Zhiyuan Li, Hong Liu, Denny Zhou, and Tengyu Ma · 2024
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Graphinstruct: Empowering large language models with graph understanding and reasoning capability
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Let your graph do the talking: Encoding structured data for llms
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Musegraph: Graph-oriented instruction tuning of large language models for generic graph mining
Yanchao Tan, Hang Lv, Xinyi Huang, Jiawei Zhang, Shiping Wang, and Carl Yang · 2024
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Graphgpt: Graph instruction tuning for large language models
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Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov · 2024
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Can graph learning improve task planning?
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