• Homogeneous networks: representative of singular type of nodes and relationships
• Challenges: multiple types of nodes and links

 

• Matapath2vec
  • meta-path based random walks
  • Heterogeneous skip-gram
• Matapath2vec++
  • Structural and semantic correlations in heterogeneous networks.

 

 

Although there are different types of nodes in V, their representations are mapped into the same latent space.

 

Homogeneous network embedding

• Structural context = local neighborhoods
• Maximize the network probability in terms of local structures:

Heterogeneous network embedding: metapath2vec

• Heterogeneous skip-gram (model the structural correlations between nodes in a path)

• Meta-path-based random walks (Transform the structure of a network into skip-gram)
  • A meta-path scheme
  • Composite relations between node types
  • Use meta-paths to guide heterogeneous random walkers, transition probability at step i:

• • The flow of the walker is conditioned on the pre-defined meta-path scheme.
  • The meta-path-based random walk strategy ensures that the semantic relationships between different types of nodes can be properly incorporated into skip-gram.

• Metapath2vec++
  • Metapath2vec ignores the node type information in softmax. In other words, metapath2vec actually encourages all types of negative samples, including nodes of the same type t as well as the other types in the heterogeneous network.
  • Heterogeneous negative sampling

• • In metapath2vec++'s skip-gram, the multinomial distribution dimension for type t nodes is determined by the number of t-type nodes.

Relevance

• Word2vec 
• Word2vec based network representation learning frameworks (homogeneous networks)
  • DeepWalk
  • LINE
  • Node2vec
• PTE
• Negative sampling
• K-means algorithm
• Logistic regression classifier
• Biased random walkers (a mixture of breadth-first and width-first search procedures )