default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID1st LoG 2022: Virtual Event
- Bastian Rieck, Razvan Pascanu:
Learning on Graphs Conference, LoG 2022, 9-12 December 2022, Virtual Event. Proceedings of Machine Learning Research 198, PMLR 2022 - Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov:
The First Learning on Graphs Conference: Preface. i-xxiii - Yuhong Luo, Pan Li:
Neighborhood-Aware Scalable Temporal Network Representation Learning. 1 - Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bosnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Velickovic:
A Generalist Neural Algorithmic Learner. 2 - Chenhui Deng, Xiuyu Li, Zhuo Feng, Zhiru Zhang:
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks. 3 - Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu:
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification. 4 - Ralph Abboud, Radoslav Dimitrov, Ismail Ilkan Ceylan:
Shortest Path Networks for Graph Property Prediction. 5 - Renming Liu, Semih Cantürk
, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew J. Hirn, Guy Wolf, Ladislav Rampásek:
Taxonomy of Benchmarks in Graph Representation Learning. 6 - Jiaqi Ma, Xingjian Zhang, Hezheng Fan, Jin Huang, Tianyue Li, Ting Wei Li, Yiwen Tu, Chenshu Zhu, Qiaozhu Mei:
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks. 7 - Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. 8 - Johannes Gasteiger, Chendi Qian, Stephan Günnemann:
Influence-Based Mini-Batching for Graph Neural Networks. 9 - Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Velickovic, Zhitao Ying, Jure Leskovec, Pietro Liò:
Learning Graph Search Heuristics. 10 - Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features. 11 - Bingxin Zhou, Xinliang Liu, Yuehua Liu, Yunying Huang, Pietro Liò, Yuguang Wang:
Well-Conditioned Spectral Transforms for Dynamic Graph Representation. 12 - Ruochen Yang, Frederic Sala, Paul Bogdan:
Efficient Representation Learning for Higher-Order Data With Simplicial Complexes. 13 - Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah:
Flashlight: Scalable Link Prediction With Effective Decoders. 14 - Adrián Arnaiz-Rodríguez, Ahmed Begga, Francisco Escolano, Nuria Oliver:
DiffWire: Inductive Graph Rewiring via the Lovász Bound. 15 - Simon Zhang, Soham Mukherjee, Tamal K. Dey:
GEFL: Extended Filtration Learning for Graph Classification. 16 - Zhaohui Wang, Qi Cao, Huawei Shen, Bingbing Xu, Muhan Zhang, Xueqi Cheng:
Towards Efficient and Expressive GNNs for Graph Classification via Subgraph-Aware Weisfeiler-Lehman. 17 - Sara Hahner, Felix Kerkhoff, Jochen Garcke:
Transfer Learning Using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes. 18 - Xiyuan Wang, Muhan Zhang:
Graph Neural Network With Local Frame for Molecular Potential Energy Surface. 19 - Franka Bause, Nils Morten Kriege:
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race. 20 - Yixuan He, Gesine Reinert, Mihai Cucuringu:
DIGRAC: Digraph Clustering Based on Flow Imbalance. 21 - Paul Scherer, Pietro Liò, Mateja Jamnik:
Distributed Representations of Graphs for Drug Pair Scoring. 22 - Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert:
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series. 23 - Mohammad Sabbaqi, Riccardo Taormina, Alan Hanjalic, Elvin Isufi:
Graph-Time Convolutional Autoencoders. 24 - Yacouba Kaloga, Pierre Borgnat, Amaury Habrard:
A Simple Way to Learn Metrics Between Attributed Graphs. 25 - Enyan Dai, Shijie Zhou, Zhimeng Guo, Suhang Wang:
Label-Wise Graph Convolutional Network for Heterophilic Graphs. 26 - Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu:
PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. 27 - Valentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez, Chirag Agarwal:
Towards Training GNNs Using Explanation Directed Message Passing. 28 - Kuangqi Zhou, Kaixin Wang, Jian Tang, Jiashi Feng, Bryan Hooi, Peilin Zhao, Tingyang Xu, Xinchao Wang:
Jointly Modelling Uncertainty and Diversity for Active Molecular Property Prediction. 29 - Tuan Le, Frank Noé, Djork-Arné Clevert:
Representation Learning on Biomolecular Structures Using Equivariant Graph Attention. 30 - Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler:
Neural Graph Databases. 31 - Tong Zhao, Xianfeng Tang, Danqing Zhang, Haoming Jiang, Nikhil Rao, Yiwei Song, Pallav Agrawal, Karthik Subbian, Bing Yin, Meng Jiang:
AutoGDA: Automated Graph Data Augmentation for Node Classification. 32 - Donato Crisostomi, Simone Antonelli, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà:
Metric Based Few-Shot Graph Classification. 33 - Guangxuan Xiao, Leslie Pack Kaelbling, Jiajun Wu, Jiayuan Mao:
Sparse and Local Networks for Hypergraph Reasoning. 34 - Zhenwei Dai, Vasileios Ioannidis, Soji Adeshina, Zak Jost, Christos Faloutsos, George Karypis:
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning. 35 - Abdulkadir Çelikkanat, Nikolaos Nakis, Morten Mørup:
Piecewise-Velocity Model for Learning Continuous-Time Dynamic Node Representations. 36 - Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang:
TopoImb: Toward Topology-Level Imbalance in Learning From Graphs. 37 - Andreea Deac, Marc Lackenby, Petar Velickovic:
Expander Graph Propagation. 38 - Xuhong Wang, Sirui Chen, Yixuan He, Minjie Wang, Quan Gan, Junchi Yan:
CEP3: Community Event Prediction With Neural Point Process on Graph. 39 - Yixuan He, Michael Perlmutter, Gesine Reinert, Mihai Cucuringu:
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian. 40 - David Montero, J. Javier Yebes:
Combining Graph and Recurrent Networks for Efficient and Effective Segment Tagging. 41 - Alexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie:
A Systematic Evaluation of Node Embedding Robustness. 42 - Euan Ong, Petar Velickovic:
Learnable Commutative Monoids for Graph Neural Networks. 43 - Kenza Amara, Zhitao Ying, Zitao Zhang, Zhichao Han, Yang Zhao, Yinan Shan, Ulrik Brandes, Sebastian Schemm, Ce Zhang:
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks. 44 - Sayak Mukherjee, Sai Pushpak Nandanoori, Sheng Guan, Khushbu Agarwal, Subhrajit Sinha, Soumya Kundu, Seemita Pal, Yinghui Wu, Draguna L. Vrabie, Sutanay Choudhury:
Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems. 45 - Pablo Barceló, Mikhail Galkin, Christopher Morris, Miguel A. Romero Orth:
Weisfeiler and Leman Go Relational. 46 - Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu:
A Survey on Deep Graph Generation: Methods and Applications. 47 - Callum Christopher Mackenzie, Muhammad Dawood, Simon Graham, Mark Eastwood, Fayyaz ul Amir Afsar Minhas:
Neural Graph Modelling of Whole Slide Images for Survival Ranking. 48 - Christoffel Doorman, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi:
Dynamic Network Reconfiguration for Entropy Maximization Using Deep Reinforcement Learning. 49 - Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell:
Reasoning-Modulated Representations. 50 - Lisi Qarkaxhija, Vincenzo Perri, Ingo Scholtes:
De Bruijn Goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs. 51 - Christopher Blöcker, Jelena Smiljanic, Ingo Scholtes, Martin Rosvall:
Similarity-Based Link Prediction From Modular Compression of Network Flows. 52 - David W. Zhang, Gertjan J. Burghouts, Cees G. M. Snoek:
Pruning Edges and Gradients to Learn Hypergraphs From Larger Sets. 53 - Yu He, Petar Velickovic, Pietro Liò, Andreea Deac:
Continuous Neural Algorithmic Planners. 54 - Simone Piaggesi, André Panisson, Giovanni Petri:
Effective Higher-Order Link Prediction and Reconstruction From Simplicial Complex Embeddings. 55 - Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla:
FakeEdge: Alleviate Dataset Shift in Link Prediction. 56
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-12-10 20:51 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
