gl2vec: learning feature representation using graphlets for directed networks. Learning network representation has a variety  :6,such as network classification. Most existing  :13,this area focuses on static undirected networks and does  :23,for presence of directed edges or temporal  :31,most work focuses on node representations  :38,poorly on tasks like network classification. In :13,this  :47,propose a novel network embedding methodology,  network :54,classification in both static and temporal  :62,gl2vec constructs vectors :23,for feature  static or temporal network :68,graphlet distributions and  :76,model :23,for comparing them against random graphs.  :84,the efficacy and usability of :62,gl2vec over  :92,methods on network :54,classification tasks :6,such  :99,type :54,classification and subgraph identification in  static and temporal directed :106,networks. We argue  :114,provides additional network features that are not  :122,stateoftheart methods, which can significantly improve  :129,accuracy by up to 10% in realworld applications

Network2Vec: Learning Node Representation Based on Space Mapping in Networks Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixedlength vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the stateoftheart model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods.