Hi, those are some other graph level classification papers for your information
Graph Kernel:
"A Graph Kernel Based on the Jensen-Shannon Representation Alignment" IJCAI 2015
Lu Bai, Zhihong Zhang, Chaoyan Wang, Xiao Bai, Edwin R. Hancock
paper: http://ijcai.org/Proceedings/15/Papers/468.pdf
code: https://github.com/baiuoy/Matlab-code-JS-alignment-kernel-IJCAI-2015

“An Aligned Subtree Kernel for Weighted Graphs” ICML 2015
Lu Bai, Luca Rossi, Zhihong Zhang, Edwin R. Hancock
paper: http://proceedings.mlr.press/v37/bai15.pdf
code will be released soon

Deep Learning:
"Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification" ECML-PKDD 2019
Lu Bai, Yuhang Jiao, Lixin Cui, Edwin R. Hancock
paper: https://arxiv.org/abs/1904.04238
code: https://github.com/baiuoy/ASGCN_ECML-PKDD2019 (will be released soon)

Hi, thanks for maintaining such a comprehensive list of methods for graph-level machine learning. I am an author of the ICDM 2019 paper "Distribution of Node Embeddings as Multiresolution Features for Graphs" and was wondering if it could be included on this list?
Overview: Derives a randomized feature map for a graph based on the distribution of its node embeddings in vector space. As the proposed technique is an explicit feature map, it probably fits in the section on "spectral and statistical fingerprints", but its theoretical underpinnings come from the graph kernel literature and so it might fit in that section instead. Won best student paper at ICDM 2019.
Paper: [https://ieeexplore.ieee.org/document/8970922]
Code: [https://github.com/GemsLab/RGM]

Attention-based pooling operator without having to learn n^2 cluster-assignment matrix as in DiffPool.
paper: https://arxiv.org/abs/1904.08082
code: https://github.com/inyeoplee77/SAGPool

Hi!

Thank you for this awesome repository!

Could you please add the following paper, code, and data link to the repository:
Paper: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
Authors: Srijan Kumar, Xikun Zhang, Jure Leskovec
Venue: ACM SIGKDD 2019 (Proceedings of the 25th ACM SIGKDD international conference on Knowledge discovery and data mining)
Project page: http://snap.stanford.edu/jodie/
Code: https://github.com/srijankr/jodie/
All datasets: http://snap.stanford.edu/jodie/

Many thanks,
Srijan

Hi there,

please add our paper “A Persistent Weisfeiler–Lehman Procedure for Graph Classification” as well to this repository:

Paper: http://proceedings.mlr.press/v97/rieck19a/rieck19a.pdf
Code: https://github.com/BorgwardtLab/P-WL

Best,
Bastian

how to generate embeddings using your library if my input is in grapson(json) or graphml(xml) format?

Hi,

in our KDD2020 work we solve a graph classification problem with nice results!

Paper: https://dl.acm.org/doi/10.1145/3394486.3403383
Code: https://github.com/tlancian/contrast-subgraph

Would you add it to the repo?

Thank you,
Tommaso

Hi,

Thank you for your paper list. I am the author of the paper A Simple Yet Effective Baseline for Non-Attribute Graph Classification. It has been accepted by ICLR 2019 graph representation learning workshop (https://rlgm.github.io/). Would you like to update the record? Thanks!

Best,
Chen

Hi, I am the author of the library py-graph. Thanks a lot for including our library! Just to inform you that we updated our library and now there are implementations for 8 graph kernels. We also upload our library to PyPI. Thanks!

You can also add to the list "Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction" from NeurIPS18.

It's a novel graph architecture for mapping images to scene graphs using a permutation invariant property, which achieves a new state-of-the-art results on Visual Genome dataset.

paper: https://arxiv.org/abs/1802.05451
code: https://github.com/shikorab/SceneGraph