Graph Embedding and Reasoning - SIGKDD · Graph Embedding and Reasoning ... unifying deepwalk,...

1

Graph Embedding and Reasoning

Jie Tang

Department of Computer Science and Technology

Tsinghua University

The slides can be downloaded at http://keg.cs.tsinghua.edu.cn/jietang

http://keg.cs.tsinghua.edu.cn/jietang

2

Networked World

• 2 billion MAU

• 26.4 billion minutes/day

• 350 million users

• influencing our daily life• 320 million MAU

• Peak: 143K tweets/s

•QQ: 860 million MAU

• WeChat: 1 billion MAU

• 700 million MAU

• 95 million pics/day

• >777 million trans. (alipay)

• 200 billion on 11/11

• 300 million MAU

• 30 minutes/user/day

• ~200 million MAU

• 70 minutes/user/day

3

Mining Big Graphs/Networks

• An information/social graph is made up of a set of

individuals/entities (“nodes”) tied by one or more

interdependency (“edges”), such as friendship.

1. David Lazer, Alex Pentland, Lada Adamic, Sinan Aral, Alber-Laszlo Barabasi, et al. Computational Social Science. Science 2009.

Mining big networks: “A field is emerging that leverages the capacity to

collect and analyze data at a scale that may reveal patterns of individual

and group behaviors.” [1]

4

Info.

Space

Social

Space

Info. Space + Social Space

Challenges

big

hetero

geneous

dynamic

Interaction

Interaction

1. J. Scott. (1991, 2000, 2012). Social network analysis: A handbook.

2. D. Easley and J. Kleinberg. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press, 2010.

Challenges

5

1930s o Sociogram [Moreno]

o Homophily [Lazarsfeld & Merton]

o Balance Theory [Heider et al.]o Random Graph [Erdos, Renyi, Gilbert]

o Degree Sequence [Tuttle, Havel, Hakami]

1950s

1960s

1970s

o Small Worlds [Migram]

o The Strength Of Weak Tie [Granovetter]

1992 o Structural Hole [Burt]

o Dunbar’s Number [Dunbar]

o Small Worlds [Watts & Strogatz]

o Scale Free [Barabasi & Albert]

o Power Law [Faloutsos 3]o HITS [Kleinberg]

o PageRank [Page & Brin]

o Hyperlink Vector Voting [Li]

1997

1998/9

2000~2004o Influence Max’n [Domingos & Kempe et al.]

o Community Detection [Girvan & Newman]

o Network Motifs [Milo et al.]

o Link Prediction [Liben-Nowell & Kleinberg]

o Graph Evolution [Leskovec et al.]

o 3 Deg. Of Influence [Christakis & Fowler]

o Social Influence Analysis [Tang et al.]

o Six Deg. Of Separation [Leskovec & Horvitz]

o Network Heterogeneity [Sun & Han]

o Network Embedding [Tang & Liu]

o Computer Social Science [Lazer et al.]

o Info. vs. Social Networks (Twitter) [Kwak et al.]

o Signed Networks [Leskovec et al.]

o Semantic Social Networks [Tang et al.]

o Four Deg. Of Separation [Backstrom et al.]

o Structural Diversity [Ugander et al.]

o Computational Social Science [Watts]

o Network Embedding [Perozzi et al.]2005~2009

2010~2014

2015~2019

o Graph Neural Networks

o Deep Learning for Networks

o High-Order Networks [Benson et al.]

Social & Information Network Analysis

The Late 20th Century:

CS & Physics

The 20th Century:

Sociology &

Anthropology

The 1st decade of the 21st

Century:

More Computer & Data

Scientists

Recent Trend:

Deep Learning for

Networks

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Recent Trend

0.8 0.2 0.3 … 0.0 0.0

d-dimensional vector, d<<|V|

Users with the same label are located in

the d-dimensional space closer than those

with different labels

label1

label2

e.g., node classification

Representation Learning/

Graph Embedding

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Example1: Open Academic Graph—how can we benefit from graph embedding?

• 189M publications• 113M researchers• 8M concepts• 8M IP accesses

data intelligenceknowledge

240M Authors

25K Institutions

220M Publications

664K Fields of Studies

Event

50K Venues

Open Academic

Graph@

Open Academic

Society

Tsinghua University AMiner Microsoft Academic Graph

Linking large-scale heterogeneous academic graphs

https://www.openacademic.ai/oag/

Open Academic Graph (OAG) is a large knowledge graph by linking two

billion-scale academic graphs: Microsoft Academic Graph (MAG) and AMiner.

1. F. Zhang, X. Liu, J. Tang, Y. Dong, P. Yao, J. Zhang, X. Gu, Y. Wang, B. Shao, R. Li and K. Wang. OAG: Toward Linking Large-scale Heterogeneous

Entity Graphs. KDD'19.

https://www.openacademic.ai/oag/

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Results

Our methods based

on graph embeddings

** Code&Data available at https://github.com/zfjsail/OAG

https://github.com/zfjsail/OAG

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Active neighbor Inactive neighbor v User to be influenced

Who are more likely to be “active”, v1 or v2?

v

A

B

D E

F

C

H v

A

B H

D E

F

C

v1 v2

e.g., active = “dropout an online forum”

Example: Online Forum

Example2: Social Prediction—how can we benefit from graph embedding?

1. J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, and J. Tang. DeepInf: Social Influence Prediction with Deep Learning. KDD'18.

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Dropout Prediction

• Dropout prediction with influence

– Problem: dropout prediction

– Game data: King of Glory (王者荣耀)

With Influence w/o influence

Top k #message #success ratio #message #success ratio

1 3996761 1953709 48.88% 6617662 2732167 41.29%

2 2567279 1272037 49.55% 9756330 4116895 42.20%

3 1449256 727728 50.21% 10537994 4474236 42.46%

4 767239 389588 50.78% 9891868 4255347 43.02%

5 3997251 2024859 50.66% 15695743 6589022 41.98%

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Outline

• Representation Learning for Graphs

– Unified graph embedding as matrix factorization

– Scalable graph embedding

– Fast graph embedding

• Extensions and Reasoning

– Dynamic

– Heterogeneous

– Reasoning

• Conclusion and Q&A

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Input:

Adjacency Matrix

𝑨

Output:

Vectors

𝒁

Representation Learning on Networks

𝑺 = 𝑓(𝑨) RLN by Matrix

Fast RLN 𝒁 = 𝑓(𝒁′)

Scalable RLN Sparsify 𝑺

offer 10-400X speedups

handle 100M graph

achieve better accuracy

1. Qiu et al. Network embedding as matrix factorization: unifying deepwalk, line, pte, and node2vec. WSDM’18. The most cited paper in WSDM’18 as of May 2019

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DeepWalk

1. B. Perozzi, R. Al-Rfou, and S. Skiena. 2014. Deepwalk: Online learning of social representations. KDD, 701–710.

v1

v2

v3 v4

v6

v5

v4 v3 v1 v5 v6

Random walk One example RW pathSkipGram with

Hierarchical softmax

Hierarchical

softmax

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Later…

• LINE[1]: explicitly preserves both first-

order and second-order proximities.

• PTE[2]: learn heterogeneous text

network embedding via a semi-

supervised manner.

• Node2vec[3]: use a biased random

walk to better explore node’s

neighborhood.

1. J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. 2015. Line: Large-scale information network embedding. WWW, 1067–1077.

2. J. Tang, M. Qu, and Q. Mei. 2015. Pte: Predictive text embedding through large-scale heterogeneous text networks. KDD, 1165–1174.

3. A. Grover and J. Leskovec. 2016. node2vec: Scalable feature learning for networks. KDD, 855–864.

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Questions

• What are the fundamentals underlying the

different models?

or

• Can we unify the different network embedding

approaches?

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Unifying DeepWalk, LINE, PTE, and

node2vec into Matrix Forms


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Starting with DeepWalk

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DeepWalk Algorithm

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Skip-gram with Negative Sampling

1. Levy and Goldberg. Neural word embeddings as implicit matrix factorization. In NIPS 2014

• SGNS maintains a multiset 𝓓 that counts the occurrence of

each word-context pair (𝑤, 𝑐)

• Objective

ℒ =

𝑤

𝑐

(# 𝑤, 𝑐 log 𝑔 𝑥𝑤𝑇𝑥𝑐 +

𝑏# 𝑤 #(𝑐)

|𝒟|log 𝑔(−𝑥𝑤

𝑇𝑥𝑐))

where xw and xc are d-dimenational vector

• For sufficiently large dimension d, the objective above is

equivalent to factorizing the PMI matrix[1]

log#(𝑤, 𝑐)|𝒟|

𝑏#(𝑤)#(𝑐)

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DeepWalk

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Understanding random walk + skip gram

Suppose the multiset 𝒟 is constructed based on random

walk on graphs, can we interpret 𝑙𝑜𝑔#(𝑤,𝑐)|𝒟|

𝑏#(𝑤)#(𝑐)with graph

structures?

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• Partition the multiset 𝒟 into several sub-multisets according to the

way in which each node and its context appear in a random walk

node sequence.

• More formally, for 𝑟 = 1, 2,⋯ , 𝑇, we define

Distinguish direction

and distance

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24


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the length of random walk 𝐿 → ∞


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27




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𝑤𝑖

𝑤𝑖−2

𝑤𝑖−1

𝑤𝑖+1

𝑤𝑖+2

DeepWalk is asymptotically and implicitly factorizing

DeepWalk is factorizing…

𝑣𝑜𝑙 𝐺 =

𝑖

𝑗

𝐴𝑖𝑗

𝑨 Adjacency matrix

𝑫 Degree matrix

b: #negative samples

T: context window size

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LINE

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PTE

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node2vec — 2nd Order Random Walk

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Unifying DeepWalk, LINE, PTE, and

node2vec into Matrix Forms


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• Can we directly factorize the derived matrix?

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NetMF: explicitly factorizing the DW matrix

𝑤𝑖

𝑤𝑖−2

𝑤𝑖−1

𝑤𝑖+1

𝑤𝑖+2

DeepWalk is asymptotically and implicitly factorizing

Matrix

Factorization


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Explicitly factorize the matrix

1. R. Lehoucq, D. Sorensen, and C.Yang. 1998. ARPACK users’ guide: solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods. SIAM.

* approximate D-1/2AD-1/2 with

its top-h eigenpairs Uh𝝠hUhT

* decompose using Arnoldi

algorithm[1]

* decompose using Arnoldi

algorithm[1]

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Error Bound Analysis for NetMF

• According to Frobenius norm’s property

• and because M’i,j=max(Mi,j, 1)>=1, we have

• Also because the property of NGL,

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Experimental Setup

** Code available at https://github.com/xptree/NetMF

https://github.com/xptree/NetMF

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Results

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Results

• NetMF significantly outperforms LINE and

DeepWalk when sparse labeled vertices are

provided

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Challenge in NetMF

Academic graphSmall world

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Input:

Adjacency Matrix

𝑨

Output:

Vectors

𝒁






handle 100M graph


1. J. Qiu, Y. Dong, H. Ma, J. Li, C. Wang, K. Wang, and J. Tang. NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. WWW’19.

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Sparsify 𝑺

For random-walk matrix polynomial

where and non-negative

One can construct a 1 + 𝜖 -spectral sparsifier ෨𝑳 with non-zeros

in time

for undirected graphs

1. D. Cheng, Y. Cheng, Y. Liu, R. Peng, and S.H. Teng, Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification, COLT 2015.

2. D. Cheng, Y. Cheng, Y. Liu, R. Peng, and S.H. Teng. Spectral sparsification of random-walk matrix polynomials. arXiv:1502.03496.

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Sparsify 𝑺




in time

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Sparsify 𝑺




in time

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Sparsify 𝑺




in time

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NetSMF --- Sparse

Factorize the constructed matrix

1. J. Qiu, Y. Dong, H. Ma, J. Li, C. Wang, K. Wang, and J. Tang. NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. WWW’19.

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NetSMF — Approximation Error

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Datasets

** Code available at https://github.com/xptree/NetSMF

https://github.com/xptree/NetSMF

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Results

** Code available at https://github.com/xptree/NetSMF

https://github.com/xptree/NetSMF

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Input:

Adjacency Matrix

𝑨

Output:

Vectors

𝒁






handle 100M graph


1. J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding. ProNE: Fast and Scalable Network Representation Learning. IJCAI’19.

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ProNE: Fast and Scalable Network Embedding

1. J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding. ProNE: Fast and Scalable Network Representation Learning. IJCAI’19.

54** Code available at https://github.com/THUDM/ProNE

* ProNE (1 thread) v.s.

Others (20 threads)

ProNE: Fast and Scalable Network Embedding

* 10 minutes on

Youtube (~1M nodes)

https://github.com/THUDM/ProNE

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NE as Sparse Matrix Factorization

• node-context set (sparsity)

• For edge (i, j), the occurrence probability of context j given

node i

• objective (weighted sum of log-loss)

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• To avoid the trivial solution

• Local negative samples drawn from

• Modify the loss (sum over the edge-->sparse)

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• Let the partial derivative w.r.t. be zero

• Matrix to be factorized (sparse)

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• Matrix to be factorized (sparse)

• Many methods for truncated SVD with the sparse matrix

(e.g., randomized tSVD, )

• This optimization method (single thread) is much faster than

SGD used in DeepWalk, LINE, etc. and is still scalable!!!

• Orthogonality, avoid direct non-convex optimization

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• Compared with matrix factorization method (e.g., NetMF)

• Sparsity (local structure and local negative samples)→

much faster and scalable

• Challenge: may lose high order information!

• Improvement via spectral propagation

V.S.

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Higher-order Cheeger’s inequality

•

• Bridge graph spectrum and graph partitioning

• k-way Cheeger constant :reflects the effect of the

graph partitioned into k parts. A smaller value of

means a better partitioning effect.

1. J. R. Lee, S. O. Gharan, L. Trevisan. Multiway spectral partitioning and higher-order cheeger inequalities. Journal of the ACM (JACM), 2014, 61(6): 37.

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Higher-order Cheeger’s inequality

• Bridge graph spectrum and graph partition

• low eigenvalues control the global information

• high eigenvalues control the local information

•

• spectral propagation: propagate the initial network

embedding in the spectrally modulated network !

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NE Enhancement via Spectral Propagation

• signal filterlow pass filter

band pass filter

high pass filter

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• signal filter (low-pass example)

64


• spectral propagation for NE

• is the spectral filter of

• is the spectral filter of

• is modulated by the filter in the spectrum

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• the form of the spectral filter

• Band-pass (low-pass, high-pass)

• pass eigenvalues within a certain range and weaken

eigenvalues outside that range

• amplify local and global network information

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Chebyshev Expansion for Efficiency

• Utilize truncated Chebyshev expansion to avoid explicit

eigendecomposition and Fourier transform

• Calculate the coefficient of Chebyshev expansion

where is the modified Bessel function of the first kind

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Chebyshev Expansion for Efficiency

• NE

• Denote and calculate the Equation in the

following recursive way

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Complexity of ProNE

• Spectral propagation only involves sparse matrix

multiplication! The complexity is linear!

• sparse matrix factorization + spectral propagation

= O(|V|d2 + k|E|)

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Datasets

** Code available at https://github.com/THUDM/ProNE


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Results


* ProNE (1 thread) v.s.

Others (20 threads)

* 10 minutes on

Youtube (~1M nodes)


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Effectiveness experiments


* ProNE (SMF) = ProNE w/

only sparse matrix factorization

Embed 100,000,000 nodes by one thread:

29 hours with performance superiority


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Spectral Propagation: k-way Cheeger

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Outline






– Dynamic

– Heterogeneous

– Reasoning


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BurstGraph: Dynamic Network Embedding

with Burst Detection

• GraphSage aggregates structural information for each snapshot of dynamic networks.

• VAE reconstructs the vanilla evolution and bursty evolution.

• RNN Structure extends the model for dynamic networks.

1. Y. Zhao, X. Wang, H. Yang, L. Song, and J. Tang. Large Scale Evolving Graphs with Burst Detection. IJCAI’19.

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Information Aggregation

• To get the feature information of each snapshot, we utilize

GraphSage to aggregate the hidden representation from vertices’

neighbors.

• In each network snapshot, GraphSage samples neighbors via

uniform distribution.

• Then GraphSage uses simple max-pooling operator to aggregate

information from neighbors.

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Vanilla Evolution

• Based on the feature information 𝐺𝑖,𝑡 aggregated by GraphSAGE, vanilla and

bursty evolution both choose VAE to reconstruct the structure of network

snapshot;

• The random variable 𝒛𝑡 of vanilla evolution follows a normal Gaussian

distribution:

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Bursty Evolution

• Due to the sparsity and contingency of bursty evolution, the random variable

𝒔𝑡 of bursty evolution follows a Spike-and-Slab distribution:

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Temporal Consistence

• Taking the temporal structure of the dynamic networks into account, we

introduce an RNN structure into our framework.

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Link Prediction Result

• Data

– Alibaba: one of the largest E-commerce platform

• Methods

– Static NE: DeepWalk, GraphSage

– Dynamic NE: TNE, CTDNE

* Task: predicting links between users and items

** Code available at https://github.com/ericZhao93/BurstGraph

https://github.com/ericZhao93/BurstGraph

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Outline






– Dynamic

– Heterogeneous

– Reasoning


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Multiplex Heterogeneous Graph Embedding

users items

click

add-to-preference

add-to-cart

conversionGender: male

Age: 23

Location: Beijing

…

Price: $1000

Brand: Lenovo

…

Gender: male

Age: 35

Location: Boston

…

Gender: female

Age: 26

Location: Bangalore

…

Price: $800

Brand:Apple

…

Price: $50

Brand: Nike

…

• Multiplex networks comprise not only multi-typed nodes and/or edges but also a rich

set of attributes.

• How do we deal with the multiplex edges?

1. Y. Cen, X. Zou, J. Zhang, H. Yang, J. Zhou and J. Tang. Representation Learning for Attributed Multiplex Heterogeneous Network. KDD’19.

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Related Network Embedding Methods

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GATNE: Multiplex Heterogeneous Graph Embedding

Generating Overall Node Embedding

Base Embedding

Edge Embedding

0 1 0 0 1

1 0 0 1 0

0 1 0 1 0

Node Attributes

GATNE-T

GATNE-I

0

0

0

0

1

0

0

0

0

0

0

0

0

Heterogeneous Skip-Gram

Input Layer

Hidden Layer

Output Layer

…

|V|-dim

|V1|×K1

…

…

|V2|×K2

|V3|×K3

1. Y. Cen, X. Zou, J. Zhang, H. Yang, J. Zhou and J. Tang. Representation Learning for Attributed Multiplex Heterogeneous Network. KDD’19.

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Link Prediction Results

• Data

– Small: Amazon, YouTube, Twitter w/ 10K nodes

– Large: Alibaba w/ 40M nodes and 0.5B edges

GATNE outperforms all sorts of baselines in the various datasets.

** Code available at https://github.com/THUDM/GATNE

https://github.com/THUDM/GATNE

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Outline






– Dynamic

– Heterogeneous

– Reasoning


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Cognitive Graph: DL + Dual Process Theory

1. M. Ding, C. Zhou, Q. Chen, H. Yang, and J. Tang. Cognitive Graph for Multi-Hop Reading Comprehension at Scale. ACL’19.

implicit

knowledge

expansion

explicit

decision

For more, please come to the DS in China Session

tomorrow morning (Summit Room 5/6)

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CogDL—A Toolkit for Deep Learning on Graphs

** Code available at https://keg.cs.tsinghua.edu.cn/cogdl/

https://keg.cs.tsinghua.edu.cn/cogdl/

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CogDL—A Toolkit for Deep Learning on Graphs

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CogDL features

• CogDL supports these features:

✓Sparsification: fast network embedding on large-scale

networks with tens of millions of nodes

✓Arbitrary: dealing with different graph structures: attributed,

multiplex and heterogeneous networks

✓Distributed: multi-GPU training on one machine or across

multiple machines

✓Extensible: easily register new datasets, models, criterions

and tasks

• Homepage: https://keg.cs.tsinghua.edu.cn/cogdl/


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CogDL Leaderboards

Multi-label Node ClassificationThe following leaderboard is built from unsupervised learning with

multi-label node classification setting.

http://keg.cs.tsinghua.edu.cn/cogdl/node-classification.html


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CogDL Leaderboards

Node Classification with Attributes.



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Leaderboards: Link Prediction

http://keg.cs.tsinghua.edu.cn/cogdl/link-prediction.html

http://keg.cs.tsinghua.edu.cn/cogdl/link-prediction.html

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Join us

• Feel free to join us with the three following ways:

✓add your data into the leaderboard

✓add your algorithm into the leaderboard

✓add your algorithm into the toolkit

• Note that, in order to collaborate with us, you may refer to the

documentation in our homepage.

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Graph Embedding: A Quick Summary

Spectral partitioning 1973: Donath & Hoffman

2005: Gori et al., IJCNN’05

2015: Duvenaud et al., NIPS’15; Kipf & Welling ICLR’17

Spectral clustering 2000: Ng et al. & Shi, Malik

2014: Perozzi et al., KDD’14

2015: Tang et al., WWW’15; Grover & Leskovec, KDD’16

PTE, metapath2vec

2013: Mikolov et al., ICLR’13word2vec (skip-gram)

LINE, node2vec

DeepWalk

2018: Velickovic et al., ICLR’18, Chen et al., ICLR 2018

NetMF & NetSMF

FastGCNs, Graph attention

Graph convolutional network

Graph neural network

2015: Tang et al., KDD’15; Dong et al., KDD’17

2018: Qiu et al., WSDM’18 & WWW’19

Neural message passing, GraphSage 2017: Gilmer et al., ICML’17; Hamilton et al., NIPS’17

2014: Bruna et al., ICLR’14 Spectral graph convolution

Gated graph neural network 2016: Li et al., ICLR’16

structure2vec 2016: Dai et al., ICML’16

2019: Zhang et al., IJCAI’19; Cen et al., KDD’19; Zhao et al., IJCAI’19ProNE, GATNE, BurstGraph

2019: Ding et al., ACL’19Cognitive Graph

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Input:

Adjacency Matrix

𝑨

Output:

Vectors

𝒁






handle 100M graph


Dynamic &

HeterogeneousCognitive Graph

CogDL: A Toolkit for Deep Learning on Graphs

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Related Publications

• Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, and Ming Ding. ProNE: Fast and Scalable Network Representation

Learning. IJCAI’19.

• Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou and Jie Tang. Representation Learning for

Attributed Multiplex Heterogeneous Network. KDD’19.

• Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li,

and Kuansan Wang. OAG: Toward Linking Large-scale Heterogeneous Entity Graphs. KDD’19.

• Yifeng Zhao, Xiangwei Wang, Hongxia Yang, Le Song, and Jie Tang. Large Scale Evolving Graphs with Burst

Detection. IJCAI’19.

• Yu Han, Jie Tang, and Qian Chen. Network Embedding under Partial Monitoring for Evolving Networks. IJCAI’19.

• Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. Cognitive Graph for Multi-Hop Reading

Comprehension at Scale. ACL’19.

• Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. NetSMF: Large-Scale

Network Embedding as Sparse Matrix Factorization. WWW'19.

• Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Modeling Influence

Locality in Large Social Networks. KDD’18.

• Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network Embedding as Matrix

Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. WSDM’18.

• Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. ArnetMiner: Extraction and Mining of

Academic Social Networks. KDD’08.

For more, please check here http://keg.cs.tsinghua.edu.cn/jietang


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Jie Tang, KEG, Tsinghua U http://keg.cs.tsinghua.edu.cn/jietang

Download all data & Codes https://keg.cs.tsinghua.edu.cn/cogdl/

Thank you！Collaborators:

Jie Zhang, Jiezhong Qiu, Ming Ding, Qibin Chen, Yifeng Zhao, Yukuo Cen, Yu

Han, Fanjin Zhang, Xu Zou, Yan Wang, et al. (THU)

Yuxiao Dong, Chi Wang, Hao Ma, Kuansan Wang (Microsoft)

Hongxia Yang, Chang Zhou, Le Song, Jingren Zhou, et al. (Alibaba)



Graph Embedding and Reasoning - SIGKDD · Graph Embedding and Reasoning ... unifying deepwalk,...

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