DeepWalk: Online Learning of Social Representations

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table

Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation

In this paper, we study the problem of Team Member Replacement: given a team of people embedded in a social network working on the same task, find a good candidate who can fit in the team after one team member becomes unavailable. We conjecture that a good team member replacement should have good skill matching as well as good structure matching. We formulate this problem using the concept of graph kernel. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall; and (b) scale sub-linearly.Comment: Initially submitted to KDD 201

이종 및 계층 구조 교차 문맥 그래프 합성곱 신경망

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 강유.Given attributed graphs, how can we accurately classify them using both topological structures and node features? Graph classification is a crucial task in data mining, especially in the bioinformatics domain where a chemical compound is represented as a graph of attributed compounds. Although there are existing methods like graph kernels or truncated random walks for graph classification, they do not give good accuracy since they consider features present at a single resolution, i.e., nodes or subgraphs. Such single resolution features result in a biased view of the graph's context, which is nearsighted or too wide, failing to capture comprehensive properties of each graph. In this paper, we propose H₂C₂GCN (Heterogeneous and Hierarchical Cross-context Graph Convolution Network), an accurate end-to-end framework for graph classification. Given multiple input graphs, H₂C₂GCN generates a multi-resolution tree that connects the given graphs by cross-context edges. It gives a unified view of multiple graphs considering both node features and topological structures. We propose a novel hierarchical graph convolutional network to extract the representation of each graph. Extensive experiments on real-world datasets show that H₂C₂GCN provides the state-of-the-art accuracy for graph classification.어떻게 구조적 특성과 노드의 레이블을 활용하여 속성 그래프를 분류 할 수 있을까? 그래프 분류는 데이터 마이닝 분야에서 중대한 과제로 여겨진다, 특히나 생물 정보 영역에서 화학 물질들이 속성 그래프로 표현되어 있는 경우에는 더욱 중요하다. 그러나 기존 연구들은 그래프 커널 방식이나 무작위 행보 방식을 사용하여, 그래프 내에 하나의 해상도 (노드 또는 부분그래프) 에 한정되어서 특징들을 고려한다. 이와 같이 하나의 해상도에 집중하여 특징을 고려할 경우 그래프 전체에 대한 편향된 시선으로 바라볼 수밖에 없다. 즉, 그래프들에 대하여 좁게 또는 넓게 바라보므로 그래프 간의 특징을 구분하는데 큰 어려움이 있다. 이 논문에서는 그래프 분류에 종단 간 학습이 가능한 H₂C₂GCN (Heterogeneous and Hierarchical Cross-context Graph Convolution Network)를 제안한다. 다수의 속성 그래프가 주어졌을 시, H₂C₂GCN는 다수의 해상도를 지닌 교차 문맥 간선이 이어진 트리를 만든다. 이를 통하여 다수의 그래프 간의 노드 레이블 및 구조적 특성의 견해를 담을 수 있다. 만들어진 트리에서 그래프 합성곱 신경망을 사용하여 하여 각 그래프의 임베딩을 추출하게 된다. 실험을 생물 정보 데이터에 대하여 평가를 하여 H₂C₂GCN가 기존 방법들에 비하여 높은 정확도를 가지는 것을 확인할 수 있다.I. Introduction 1 II. Related Works 5 III. Proposed Method 7 3.0.1 Overview 7 3.0.2 Multi-Resolution Mapping 10 3.0.3 Cross-Context Mapping 11 3.0.4 Hierarchical GCN 13 IV. Experiments 15 4.0.1 Experimental Settings 15 4.0.2 Classification Accuracy 19 4.0.3 Model Depth 19 4.0.4 Ablation Study 20 V. Conclusion 22 References 23 Abstract in Korean 25Maste