思源编辑

想要了解图或图神经网络?没有比看论文更好的方式了

图嵌入、图表征、图分类、图神经网络,这篇文章将介绍你需要的图建模论文,当然它们都有配套实现的。

图是一种非常神奇的表示方式,生活中绝大多数的现象或情境都能用图来表示,例如人际关系网、道路交通网、信息互联网等等。正如马哲介绍事物具有普遍联系性,而图正好能捕捉这种联系,所以用它来描述这个世界是再好不过的方法。

但图这种结构化数据有个麻烦的地方,我们先要有图才能进行后续的计算。但图的搭建并不简单,目前也没有比较好的自动化方法,所以第一步还是需要挺多功夫的。只要各节点及边都确定了,那么图就是一种非常强大且复杂的工具,模型也能推断出图中的各种隐藏知识。

不同时期的图建模

其实,我们可以将图建模分为图神经网络与传统的图模型。其中以前的图建模主要借助 Graph Embedding 为不同的节点学习低维向量表征,这借鉴了 NLP 中词嵌入的思想。而图神经网络借助深度学习进行更强大的图运算与图表征。

Graph Embedding 算法聚焦在如何对网络节点进行低维向量表示,相似的节点在表征空间中更加接近。相比之下,GNN 最大的优势在于它不只可以对一个节点进行语义表示。

例如 GNN 可以表示子图的语义信息,将网络中一小部分节点构成的语义表示出来,这是以前 Graph Embedding 不容易做到的。GNN 还可以在整个图网络上进行信息传播、聚合等建模,也就是说它可以把图网络当成一个整体进行建模。此外,GNN 对单个节点的表示也可以做得更好,因为它可以更好地建模周围节点丰富信息。

在传统图建模中,随机游走、最短路径等图方法会利用符号知识,但这些方法并没有办法很好地利用每个节点的语义信息。而深度学习技术更擅长处理非结构文本、图像等数据。简言之,我们可以将 GNN 看做将深度学习技术应用到符号表示的图数据上,或者说是从非结构化数据扩展到了结构化数据。GNN 能够充分融合符号表示和低维向量表示,发挥两者优势。

图建模论文与代码

在 GitHub 的一项开源工作中,开发者收集了图建模相关的论文与实现,并且从经典的 Graph Embedding、Graph Kernel 到图神经网络都有涉及。它们在图嵌入、图分类、图表征等领域都是非常重要的论文。

项目地址:https://github.com/benedekrozemberczki/awesome-graph-classification

该项目主要收集的论文领域如下所示:

1. Factorization

2. Spectral and Statistical Fingerprints

3. Graph Neural Network

4. Graph Kernels

因式分解

  • Learning Graph Representation via Frequent Subgraphs (SDM 2018)

    • Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung

    • Paper:https://epubs.siam.org/doi/10.1137/1.9781611975321.35

    • Python:https://github.com/nphdang/GE-FSG

  • Anonymous Walk Embeddings (ICML 2018)

    • Sergey Ivanov and Evgeny Burnaev

    • Paper:https://arxiv.org/pdf/1805.11921.pdf

    • Python:https://github.com/nd7141/AWE

  • Graph2vec (MLGWorkshop 2017)

    • Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan

    • Paper:https://arxiv.org/abs/1707.05005

    • Python High Performance:https://github.com/benedekrozemberczki/graph2vec

    • Python Reference:https://github.com/MLDroid/graph2vec_tf

  • Subgraph2vec (MLGWorkshop 2016)

    • Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan

    • Paper:https://arxiv.org/abs/1606.08928

    • Python High Performance:https://github.com/MLDroid/subgraph2vec_gensim

    • Python Reference:https://github.com/MLDroid/subgraph2vec_tf

  • Rdf2Vec: RDF Graph Embeddings for Data Mining (ISWC 2016)

    • Petar Ristoski and Heiko Paulheim

    • Paper:https://link.springer.com/chapter/10.1007/978-3-319-46523-4_30

    • Python Reference:https://github.com/airobert/RDF2VecAtWebScale

  • Deep Graph Kernels (KDD 2015)

    • Pinar Yanardag and S.V.N. Vishwanathan

    • Paper:https://dl.acm.org/citation.cfm?id=2783417

    • Python Reference:https://github.com/pankajk/Deep-Graph-Kernels

Spectral and Statistical Fingerprints

  • A Simple Yet Effective Baseline for Non-Attribute Graph Classification (ICLR RLPM 2019)

    • Chen Cai, Yusu Wang

    • Paper:https://arxiv.org/abs/1811.03508

    • Python Reference:https://github.com/Chen-Cai-OSU/LDP

  • NetLSD (KDD 2018)

    • Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller

    • Paper:https://arxiv.org/abs/1805.10712

    • Python Reference:https://github.com/xgfs/NetLSD

  • A Simple Baseline Algorithm for Graph Classification (Relational Representation Learning, NIPS 2018)

    • Nathan de Lara and Edouard Pineau

    • Paper:https://arxiv.org/pdf/1810.09155.pdf

    • Python Reference:https://github.com/edouardpineau/A-simple-baseline-algorithm-for-graph-classification

  • Multi-Graph Multi-Label Learning Based on Entropy (Entropy NIPS 2018)

    • Zixuan Zhu and Yuhai Zhao

    • Paper:https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning/blob/master/entropy-20-00245.pdf

    • Python Reference:https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning

  • Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (NIPS 2017)

    • Saurabh Verma and Zhi-Li Zhang

    • Paper:https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs.pdf

    • Python Reference:https://github.com/vermaMachineLearning/FGSD

  • Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2015)

    • Shirui Pan, Jia Wu, Xingquan Zhuy, Chengqi Zhang, and Philip S. Yuz

    • Paper:https://ieeexplore.ieee.org/document/7302040

    • Java Reference:https://github.com/shiruipan/MTG

  • NetSimile: A Scalable Approach to Size-Independent Network Similarity (arXiv 2012)

    • Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, and Christos Faloutsos

    • Paper:https://arxiv.org/abs/1209.2684

    • Python:https://github.com/kristyspatel/Netsimile

图神经网络

  • Self-Attention Graph Pooling (ICML 2019)

    • Junhyun Lee, Inyeop Lee, Jaewoo Kang

    • Paper:https://arxiv.org/abs/1904.08082

    • Python Reference:https://github.com/inyeoplee77/SAGPool

  • Variational Recurrent Neural Networks for Graph Classification (ICLR 2019)

    • Edouard Pineau, Nathan de Lara

    • Paper:https://arxiv.org/abs/1902.02721

    • Python Reference:https://github.com/edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification

  • Crystal Graph Neural Networks for Data Mining in Materials Science (Arxiv 2019)

    • Takenori Yamamoto

    • Paper:https://storage.googleapis.com/rimcs_cgnn/cgnn_matsci_May_27_2019.pdf

    • Python Reference:https://github.com/Tony-Y/cgnn

  • Explainability Techniques for Graph Convolutional Networks (ICML 2019)

    • Federico Baldassarre, Hossein Azizpour

    • Paper:https://128.84.21.199/pdf/1905.13686.pdf

    • Python Reference:https://github.com/gn-exp/gn-exp

  • Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)

    • Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang

    • Paper:https://arxiv.org/pdf/1904.05003.pdf

    • Python Reference:https://github.com/benedekrozemberczki/SEAL-CI

  • Capsule Graph Neural Network (ICLR 2019)

    • Zhang Xinyi and Lihui Chen

    • Paper:https://openreview.net/forum?id=Byl8BnRcYm

    • Python Reference:https://github.com/benedekrozemberczki/CapsGNN

  • How Powerful are Graph Neural Networks? (ICLR 2019)

    • Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka

    • Paper:https://arxiv.org/abs/1810.00826

    • Python Reference:https://github.com/weihua916/powerful-gnns

  • Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (AAAI 2019)

    • Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

    • Paper:https://arxiv.org/pdf/1810.02244v2.pdf

    • Python Reference:https://github.com/k-gnn/k-gnn

  • Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations (Arxiv 2019)

    • Marcelo Daniel Gutierrez Mallea, Peter Meltzer, and Peter J Bentley

    • Paper:https://arxiv.org/pdf/1902.08399v1.pdf

    • Python Reference:https://github.com/BraintreeLtd/PatchyCapsules

  • Three-Dimensionally Embedded Graph Convolutional Network for Molecule Interpretation (Arxiv 2018)

    • Hyeoncheol Cho and Insung. S. Choi

    • Paper:https://arxiv.org/abs/1811.09794

    • Python Reference:https://github.com/blackmints/3DGCN

  • Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018)

    • Yu Jin and Joseph F. JaJa

    • Paper:https://arxiv.org/pdf/1805.07683v4.pdf

    • Python Reference:https://github.com/yuj-umd/graphRNN

  • Graph Capsule Convolutional Neural Networks (ICML 2018)

    • Saurabh Verma and Zhi-Li Zhang

    • Paper:https://arxiv.org/abs/1805.08090

    • Python Reference:https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks

  • Graph Classification Using Structural Attention (KDD 2018)

    • John Boaz Lee, Ryan Rossi, and Xiangnan Kong

    • Paper:http://ryanrossi.com/pubs/KDD18-graph-attention-model.pdf

    • Python Pytorch Reference:https://github.com/benedekrozemberczki/GAM

  • Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)

    • Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec

    • Paper:https://arxiv.org/abs/1806.02473

    • Python Reference:https://github.com/bowenliu16/rl_graph_generation

  • Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)

    • Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton and Jure Leskovec

    • Paper:http://papers.nips.cc/paper/7729-hierarchical-graph-representation-learning-with-differentiable-pooling.pdf

    • Python Reference:https://github.com/rusty1s/pytorch_geometric

  • Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)

    • Davide Bacciu, Federico Errica, and Alessio Micheli

    • Paper:https://arxiv.org/pdf/1805.10636.pdf

    • Python Reference:https://github.com/diningphil/CGMM

  • MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)

    • Nicola De Cao and Thomas Kipf

    • Paper:https://arxiv.org/pdf/1805.11973.pdf

    • Python Reference:https://github.com/nicola-decao/MolGAN

  • Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (2018)

    • Seongok Ryu, Jaechang Lim, and Woo Youn Kim

    • Paper:https://arxiv.org/abs/1805.10988

    • Python Reference:https://github.com/SeongokRyu/Molecular-GAT

  • Compound-protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences (Bioinformatics 2018)

    • Masashi Tsubaki, Kentaro Tomii, and Jun Sese

    • Paper:https://academic.oup.com/bioinformatics/article/35/2/309/5050020

    • Python Reference:https://github.com/masashitsubaki/CPI_prediction

    • Python Reference:https://github.com/masashitsubaki/GNN_molecules

    • Python Alternative:https://github.com/xnuohz/GCNDTI

  • Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)

    • Pau Riba, Andreas Fischer, Josep Llados, and Alicia Fornes

    • Paper:https://ieeexplore.ieee.org/abstract/document/8545310

    • Python Reference:https://github.com/priba/siamese_ged

  • Edge Attention-based Multi-Relational Graph Convolutional Networks (2018)

    • Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi and Jinbo Bi

    • Paper:https://arxiv.org/abs/1802.04944v1

    • Python Reference:https://github.com/Luckick/EAGCN

  • Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)

    • Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu and Xiaoyan Zhu

    • Paper:http://coai.cs.tsinghua.edu.cn/hml/media/files/2018_commonsense_ZhouHao_3_TYVQ7Iq.pdf

    • Python Reference:https://github.com/tuxchow/ccm

  • Residual Gated Graph ConvNets (ICLR 2018)

    • Xavier Bresson and Thomas Laurent

    • Paper:https://arxiv.org/pdf/1711.07553v2.pdf

    • Python Pytorch Reference:https://github.com/xbresson/spatial_graph_convnets

  • An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)

    • Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen

    • Paper:https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf

    • Python Tensorflow Reference:https://github.com/muhanzhang/DGCNN

    • Python Pytorch Reference:https://github.com/muhanzhang/pytorch_DGCNN

    • MATLAB Reference:https://github.com/muhanzhang/DGCNN

    • Python Alternative:https://github.com/leftthomas/DGCNN

    • Python Alternative:https://github.com/hitlic/DGCNN-tensorflow

  • SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)

    • Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein and Emmanueal Müller

    • Paper:https://arxiv.org/abs/1807.02839

    • Python Reference:http://mott.in/publications/others/sgr/

  • Deep Learning with Topological Signatures (NIPS 2017)

    • Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl

    • paper:https://arxiv.org/abs/1707.04041

    • Python Reference:https://github.com/c-hofer/nips2017

  • Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)

    • Martin Simonovsky and Nikos Komodakis

    • paper:https://arxiv.org/pdf/1704.02901v3.pdf

    • Python Reference:https://github.com/mys007/ecc

  • Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)

    • Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola

    • Paper:https://arxiv.org/abs/1705.09037

    • Python Reference:https://github.com/taolei87/icml17_knn

  • Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)

    • Alex Fout, Jonathon Byrd, Basir Shariat and Asa Ben-Hur

    • Paper:https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks

    • Python Reference:https://github.com/fouticus/pipgcn

  • Graph Classification with 2D Convolutional Neural Networks (2017)

    • Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis

    • Paper:https://arxiv.org/abs/1708.02218

    • Python Reference:https://github.com/Tixierae/graph_2D_CNN

  • CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)

    • Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

    • Paper:https://arxiv.org/pdf/1705.07664v2.pdf

    • Python Reference:https://github.com/fmonti/CayleyNet

  • Semi-supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (2017)

    • Hai Nguyen, Shin-ichi Maeda, Kenta Oono

    • Paper:https://arxiv.org/pdf/1711.10168.pdf

    • Python Reference:https://github.com/pfnet-research/hierarchical-molecular-learning

  • Kernel Graph Convolutional Neural Networks (2017)

    • Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis

    • Paper:https://arxiv.org/pdf/1710.10689.pdf

    • Python Reference:https://github.com/giannisnik/cnn-graph-classification

  • Deep Topology Classification: A New Approach For Massive Graph Classification (IEEE Big Data 2016)

    • Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough

    • Paper:https://ieeexplore.ieee.org/document/7840988/

    • Python Reference:https://github.com/sbonner0/DeepTopologyClassification

  • Learning Convolutional Neural Networks for Graphs (ICML 2016)

    • Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov

    • Paper:https://arxiv.org/abs/1605.05273

    • Python Reference:https://github.com/tvayer/PSCN

  • Gated Graph Sequence Neural Networks (ICLR 2016)

    • Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel

    • Paper:https://arxiv.org/abs/1511.05493

    • Python TensorFlow:https://github.com/bdqnghi/ggnn.tensorflow

    • Python PyTorch:https://github.com/JamesChuanggg/ggnn.pytorch

    • Python Reference:https://github.com/YunjaeChoi/ggnnmols

  • Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)

    • David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams

    • Paper:https://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf

    • Python Reference:https://github.com/fllinares/neural_fingerprints_tf

    • Python Reference:https://github.com/jacklin18/neural-fingerprint-in-GNN

    • Python Reference:https://github.com/HIPS/neural-fingerprint

    • Python Reference:https://github.com/debbiemarkslab/neural-fingerprint-theano

Graph Kernels

  • Message Passing Graph Kernels (2018)

    • Giannis Nikolentzos, Michalis Vazirgiannis

    • Paper:https://arxiv.org/pdf/1808.02510.pdf

    • Python Reference:https://github.com/giannisnik/message_passing_graph_kernels

  • Matching Node Embeddings for Graph Similarity (AAAI 2017)

    • Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis

    • Paper:https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494

  • Global Weisfeiler-Lehman Graph Kernels (2017)

    • Christopher Morris, Kristian Kersting and Petra Mutzel

    • Paper:https://arxiv.org/pdf/1703.02379.pdf

    • C++ Reference:https://github.com/chrsmrrs/glocalwl

  • On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016)

    • Nils Kriege, Pierre-Louis Giscard, Richard Wilson

    • Paper:https://arxiv.org/pdf/1606.01141.pdf

    • Java Reference:https://github.com/nlskrg/optimal_assignment_kernels

  • Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)

    • Stephen Bonner, John Brennan, and A. Stephen McGough

    • Paper:http://dro.dur.ac.uk/19773/1/19773.pdf?DDD10+lzdh59+d700tmt

    • python Reference:https://github.com/sbonner0/GraphFingerprintComparison

  • The Multiscale Laplacian Graph Kernel (NIPS 2016)

    • Risi Kondor and Horace Pan

    • Paper:https://arxiv.org/abs/1603.06186

    • C++ Reference:https://github.com/horacepan/MLGkernel

  • Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)

    • Christopher Morris, Nils M. Kriege, Kristian Kersting and Petra Mutzel

    • Paper:https://arxiv.org/abs/1610.00064

    • Python Reference:https://github.com/chrsmrrs/hashgraphkernel

  • Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)

    • Neumann, Marion and Garnett, Roman and Bauckhage, Christian and Kersting, Kristian

    • Paper:https://link.springer.com/article/10.1007/s10994-015-5517-9

    • Matlab Reference:https://github.com/marionmari/propagation_kernels

  • Halting Random Walk Kernels (NIPS 2015)

    • Mahito Sugiyama and Karsten M. Borgward

    • Paper:https://pdfs.semanticscholar.org/79ba/8bcfbf9496834fdc22a1f7c96d26d776cd6c.pdf

    • C++ Reference:https://github.com/BorgwardtLab/graph-kernels

  • Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)

    • Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt

    • Paper:https://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf

  • Subgraph Matching Kernels for Attributed Graphs (ICML 2012)

    • Nils Kriege and Petra Mutzel

    • Paper:https://arxiv.org/abs/1206.6483

    • Python Reference:https://github.com/mockingbird2/GraphKernelBenchmark

  • Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)

    • Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang

    • Paper:https://ieeexplore.ieee.org/document/6413884/

    • Python Reference:https://github.com/benedekrozemberczki/NestedSubtreeHash

  • Weisfeiler-Lehman Graph Kernels (JMLR 2011)

    • Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt

    • Paper:http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf

    • Python Reference:https://github.com/jajupmochi/py-graph

    • Python Reference:https://github.com/deeplego/wl-graph-kernels

    • C++ Reference:https://github.com/BorgwardtLab/graph-kernels

  • Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)

    • Fabrizio Costa and Kurt De Grave

    • Paper:https://icml.cc/Conferences/2010/papers/347.pdf

    • C++ Reference:https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/www.bioinf.uni-freiburg.de/~costa/EDeNcpp.tgz

    • Python Reference:https://github.com/fabriziocosta/EDeN

  • A Linear-time Graph Kernel (ICDM 2009)

    • Shohei Hido and Hisashi Kashima

    • Paper:https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5360243

    • Python Reference:https://github.com/hgascon/adagio

  • Weisfeiler-Lehman Subtree Kernels (NIPS 2009)

    • Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt

    • Paper:http://papers.nips.cc/paper/3813-fast-subtree-kernels-on-graphs.pdf

    • Python Reference:https://github.com/jajupmochi/py-graph

    • Python Reference:https://github.com/deeplego/wl-graph-kernels

    • C++ Reference:https://github.com/BorgwardtLab/graph-kernels

  • Fast Computation of Graph Kernels (NIPS 2006)

    • S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph

    • Paper:http://www.dbs.ifi.lmu.de/Publikationen/Papers/VisBorSch06.pdf

    • Python Reference:https://github.com/jajupmochi/py-graph

    • C++ Reference:https://github.com/BorgwardtLab/graph-kernels

  • Shortest-Path Kernels on Graphs (ICDM 2005)

    • Karsten M. Borgwardt and Hans-Peter Kriegel

    • Paper:https://www.ethz.ch/content/dam/ethz/special-interest/bsse/borgwardt-lab/documents/papers/BorKri05.pdf

    • C++ Reference:https://github.com/KitwareMedical/ITKTubeTK

  • Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)

    • Tamás Horváth, Thomas Gärtner, and Stefan Wrobel

    • Paper:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.6158&rep=rep1&type=pdf

    • Python Reference:https://github.com/jajupmochi/py-graph

  • Extensions of Marginalized Graph Kernels (ICML 2004)

    • Pierre Mahe, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert

    • Paper:http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf

    • Python Reference:https://github.com/jajupmochi/py-graph

  • Marginalized Kernels Between Labeled Graphs (ICML 2003)

    • Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi

    • Paper:https://pdfs.semanticscholar.org/2dfd/92c808487049ab4c9b45db77e9055b9da5a2.pdf

    • Python Reference:https://github.com/jajupmochi/py-graph

理论深度学习智能科研图表示学习图分类图嵌入图神经网络
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深度学习技术

深度学习(deep learning)是机器学习的分支,是一种试图使用包含复杂结构或由多重非线性变换构成的多个处理层对数据进行高层抽象的算法。 深度学习是机器学习中一种基于对数据进行表征学习的算法,至今已有数种深度学习框架,如卷积神经网络和深度置信网络和递归神经网络等已被应用在计算机视觉、语音识别、自然语言处理、音频识别与生物信息学等领域并获取了极好的效果。

词嵌入技术

词嵌入是自然语言处理(NLP)中语言模型与表征学习技术的统称。概念上而言,它是指把一个维数为所有词的数量的高维空间嵌入到一个维数低得多的连续向量空间中,每个单词或词组被映射为实数域上的向量。

TensorFlow技术

TensorFlow是一个开源软件库,用于各种感知和语言理解任务的机器学习。目前被50个团队用于研究和生产许多Google商业产品,如语音识别、Gmail、Google 相册和搜索,其中许多产品曾使用过其前任软件DistBelief。

图神经网络技术

图网络即可以在社交网络或其它基于图形数据上运行的一般深度学习架构,它是一种基于图结构的广义神经网络。图网络一般是将底层图形作为计算图,并通过在整张图上传递、转换和聚合节点特征信息,从而学习神经网络基元以生成单节点嵌入向量。生成的节点嵌入向量可作为任何可微预测层的输入,并用于节点分类或预测节点之间的连接,完整的模型可以通过端到端的方式训练。

因式分解技术

在数学中,把一个数学因子(比如数字,多项式,或矩阵)分解其他数学因子的乘积。比如:整数15可以分解成两个质数3和5的乘积,一个多项式x^2 -4 可被因式分解为(x+2)(x-2)。

图网络技术

2018年6月,由 DeepMind、谷歌大脑、MIT 和爱丁堡大学等公司和机构的 27 位科学家共同提交了论文《Relational inductive biases, deep learning, and graph networks》,该研究提出了一个基于关系归纳偏置的 AI 概念:图网络(Graph Networks)。研究人员称,该方法推广并扩展了各种神经网络方法,并为操作结构化知识和生成结构化行为提供了新的思路。

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