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DeepWalk: Online Learning of Social Representations

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SOYEON KIM
SOYEON KIM

Paper review "DeepWalk: Online Learning of Social Representations", ACM SIG-KDD 2014

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DeepWalk: Online Learning of Social Representations Bryan Perozzi, Rami Al-Rfou, Steven Skiena ACM SIG-KDD 2014 1
Outline • Introduction: Graphs as Features • Language Modeling • DeepWalk • Evaluation: Network Classification • Conclusions & Future Work 2
Introduction • Deep Learning을 이용해 graph의 인접 행렬의 latent representation(social relations-> vector space)을 학습함 3
Introduction • 소셜 네트워크(커뮤니티) 상의 유저간의 상호작용을 vector space에서 표현하여 학습 모델에 적용 4 Zachary’s Karate Network
Language modeling • Corpus 안에서 특정 word sequence가 얼마나 나오는지 • 문서상에서 word의 latent representation을 학습함(word co-occurrence): – word2vec: • 단어의 semantic한 의미를 나타낼 수 있다 5
From language modeling to graphs • 자연어 corpus에서 단어의 빈도가 power law를 따른다 • scale-free graph의 Random walk에서 vertex frequency 역시 power law를 따른다 6
From language modeling to graphs • 각각의 random walk는 한 문장을 나타낸다 • Short random walks = sentences 7
Framework 8
Random Walks 1. 각각의 vertex(root)에서 𝛾 개의 random walks 를 생성한다 2. 각 random walk의 길이는 𝑡 이다 3. vertex의 이웃들로부터 다음으로 방문할 vertex를 선택한다(uniformly sample) 9
Framework 10
Representation Mapping • 𝑣1 을 representation vector에 mapping한다 -> Φ(𝑣1) • 이웃 vertex들을 vector space상에 mapping한다. 11 Maximize: Pr 𝑣3 Φ 𝑣1 Pr 𝑣5 Φ 𝑣1
Skip-gram model 12 한 문장에서 window size (w) 내에 등장하는 단어들의 동시 발생 확률을 최대화하는 언어 모델 Φ(𝑉1) Maximize: Pr 𝑣3 Φ 𝑣1 Pr 𝑣5 Φ 𝑣1
Framework 13
Hierarchical Softmax • Pr 𝑣3 Φ 𝑣1 을 계산하는 데에는 vertex의 수 만큼의 operation이 필요하다 -> 𝑂(𝑉) 14 • vertex를 binary tree에 표현 • Pr 𝑣3 Φ 𝑣1 를 최대화한다 = root에서 node까지 가는 path의 발생 확률을 최대화한다 • 𝑪 𝟏, 𝑪 𝟐, 𝑪 𝟑는 logistic binary classifier • 따라서, 𝑂(𝑉) -> 𝑂 log 𝑉
Learning • Learned parameters: – Vertex representations – Tree binary classifiers weights • vertex representation은 먼저 random하게 초기화한다. • 𝑪 𝟏, 𝑪 𝟐, 𝑪 𝟑와 같은 classifier가 loss function을 계산한다 • Stochastic Gradient Descent(SGD) 를 이용해 parameter를 동시에 update한다. 15
Framework 16
Experiments • Node Classification – graph의 node중 일부만 label을 가지고 있을 때, label이 없는 node들을 예측한다 • DataSet – BlogCatalog – Flickr – YouTube • Baselines – SpectralClustering, MaxModularity, EdgeCluster(k- means), weighted vote Relational Neighbor(wvRN) 17
Results: BlogCatalog • DeepWalk 는 label이 있는 node가 적은 data에서도 잘 동작한다 18
Results: Youtube • Scalable on very large graph! 19
Parallelization • 모델 학습 과정에서 parameter를 공유하지 않으므로 각 부분을 병렬처리해도 성능에 영향을 주지 않는다 20
Conclusions • Network로 표현되는 데이터를 continuous vector space 상에서 표현하여 학습이 가능하다. • Word sequence들을 graph로 표현하여 language model에 사용 가능하다. • label이 부족한 경우에도 잘 작동하다. • 큰 graph에도 Scalable하기 때문에 online learning에 사용 가능하다 21
Thank you! Q & A 22

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DeepWalk: Online Learning of Social Representations

  • 1. DeepWalk: Online Learning of Social Representations Bryan Perozzi, Rami Al-Rfou, Steven Skiena ACM SIG-KDD 2014 1
  • 2. Outline • Introduction: Graphs as Features • Language Modeling • DeepWalk • Evaluation: Network Classification • Conclusions & Future Work 2
  • 3. Introduction • Deep Learning을 이용해 graph의 인접 행렬의 latent representation(social relations-> vector space)을 학습함 3
  • 4. Introduction • 소셜 네트워크(커뮤니티) 상의 유저간의 상호작용을 vector space에서 표현하여 학습 모델에 적용 4 Zachary’s Karate Network
  • 5. Language modeling • Corpus 안에서 특정 word sequence가 얼마나 나오는지 • 문서상에서 word의 latent representation을 학습함(word co-occurrence): – word2vec: • 단어의 semantic한 의미를 나타낼 수 있다 5
  • 6. From language modeling to graphs • 자연어 corpus에서 단어의 빈도가 power law를 따른다 • scale-free graph의 Random walk에서 vertex frequency 역시 power law를 따른다 6
  • 7. From language modeling to graphs • 각각의 random walk는 한 문장을 나타낸다 • Short random walks = sentences 7
  • 9. Random Walks 1. 각각의 vertex(root)에서 𝛾 개의 random walks 를 생성한다 2. 각 random walk의 길이는 𝑡 이다 3. vertex의 이웃들로부터 다음으로 방문할 vertex를 선택한다(uniformly sample) 9
  • 11. Representation Mapping • 𝑣1 을 representation vector에 mapping한다 -> Φ(𝑣1) • 이웃 vertex들을 vector space상에 mapping한다. 11 Maximize: Pr 𝑣3 Φ 𝑣1 Pr 𝑣5 Φ 𝑣1
  • 12. Skip-gram model 12 한 문장에서 window size (w) 내에 등장하는 단어들의 동시 발생 확률을 최대화하는 언어 모델 Φ(𝑉1) Maximize: Pr 𝑣3 Φ 𝑣1 Pr 𝑣5 Φ 𝑣1
  • 14. Hierarchical Softmax • Pr 𝑣3 Φ 𝑣1 을 계산하는 데에는 vertex의 수 만큼의 operation이 필요하다 -> 𝑂(𝑉) 14 • vertex를 binary tree에 표현 • Pr 𝑣3 Φ 𝑣1 를 최대화한다 = root에서 node까지 가는 path의 발생 확률을 최대화한다 • 𝑪 𝟏, 𝑪 𝟐, 𝑪 𝟑는 logistic binary classifier • 따라서, 𝑂(𝑉) -> 𝑂 log 𝑉
  • 15. Learning • Learned parameters: – Vertex representations – Tree binary classifiers weights • vertex representation은 먼저 random하게 초기화한다. • 𝑪 𝟏, 𝑪 𝟐, 𝑪 𝟑와 같은 classifier가 loss function을 계산한다 • Stochastic Gradient Descent(SGD) 를 이용해 parameter를 동시에 update한다. 15
  • 17. Experiments • Node Classification – graph의 node중 일부만 label을 가지고 있을 때, label이 없는 node들을 예측한다 • DataSet – BlogCatalog – Flickr – YouTube • Baselines – SpectralClustering, MaxModularity, EdgeCluster(k- means), weighted vote Relational Neighbor(wvRN) 17
  • 18. Results: BlogCatalog • DeepWalk 는 label이 있는 node가 적은 data에서도 잘 동작한다 18
  • 19. Results: Youtube • Scalable on very large graph! 19
  • 20. Parallelization • 모델 학습 과정에서 parameter를 공유하지 않으므로 각 부분을 병렬처리해도 성능에 영향을 주지 않는다 20
  • 21. Conclusions • Network로 표현되는 데이터를 continuous vector space 상에서 표현하여 학습이 가능하다. • Word sequence들을 graph로 표현하여 language model에 사용 가능하다. • label이 부족한 경우에도 잘 작동하다. • 큰 graph에도 Scalable하기 때문에 online learning에 사용 가능하다 21