실세계 그래프 특징을 활용한 랜덤 워크 기반 대규모 그래프 마이닝

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 강유.Numerous real-world relationships are represented as graphs such as social networks, hyperlink networks, and protein interaction networks. Analyzing those networks is important to understand the real-life phenomena. Among various graph analysis techniques, random walk has been widely used in many applications with satisfactory results. However, various real-world graphs are large and complicated with diverse labels. Traditional random walk based methods require heavy computational cost, and disregards those labels for performing random walks; thus, its utilization has been limited in such large and complicated graphs. In this thesis, I handle the technical challenges of mining large real-world graphs based on random walk. Real-world graphs have distinct structural properties which become a basis to increase the performance of the random walk in terms of speed and quality. Based upon this idea, I develop fast, scalable, and exact methods for node ranking using random walk in large-scale plain networks. I also design accurate models using random walks for node ranking and relational reasoning in labeled graphs such as signed networks and knowledge bases. Through extensive experiments on various real-world graphs, I demonstrate the effectiveness of the methods and models proposed by this thesis. The proposed methods process 100 times larger graphs, and require up to 130 times less memory with up to 9 times faster speed compared to other existing methods, successfully scaling to billion-scale graphs. Also, the proposed models substantially improve the predictive performance of a variety of tasks in labeled graphs such as signed networks and knowledge bases.다양한 실세계 자연 현상에서의 관계들은 소셜 네트워크, 하이퍼링크 네트워크와 단백질 상호작용 네트워크와 같이 정점과 간서의 그래프로 표현된다. 이러한 네트워크를 분석하는 것은 실세계의 현상을 이해하는데 매우 중요하다. 다양한 그래프 분석 기법중에 랜덤 워크라는 기법이 만족스러운 성능과 함께 많은 그래프 마이닝 응용에 널리 활용되어 왔다. 그러나 대다수의 실세계 그래프는 그 규모가 굉장히 크고 다양한 라벨 정보와 함께 복잡하게 표현된다. 전통적인 랜덤 워크 기반의 기법들은 계산량이 많이 요구되고, 랜덤 워크를 하는데 있어서 다양한 라벨 정보를 전혀 고려하지 않아 라벨로 표현되는 그래프의 고유한 특성이 무시되게 된다. 그래서 이와 같이 복잡하면서 대규모 그래프에서는 랜덤 워크의 실질적 활용이 제한되어왔다. 본 학위 논문에서는 랜덤 워크 기반의 대규모 실세계 그래프 분석의 기술적 한계를 해결하고자 한다. 실세계 그래프는 고유한 구조적 특징들을 가지고 있으며 이러한 구조적 특징들은 속도와 품질의 측면에서 랜덤 워크의 성능을 향상시키는데 기반이 될 수 있다. 이러한 아이디어를 활용하여, 대규모의 라벨이 없는 일반적인 네트워크에서 랜덤 워크 기반의 개인화된 정점 랭킹 계산을 빠르고, 확장성 있고 정확하게 구하는 기법을 제안한다. 또한 부호화된 네트워크 또는 지식 베이스와 같은 라벨이 있는 그래프에서 개인화된 정점 랭킹과 관계 추론을 위한 랜덤 워크 기반의 모델을 제안한다. 다양한 실세계 그래프에서 광범위한 실험을 통해 본 학위 논문에 의해 제안된 방법과 모델의 효과성을 보인다. 제안하는 방법은 다른 경쟁 기법들과 비교했을 때 최대 100배 더 큰 그래프를 처리할 수 있고, 최대 130배 적게 메모리를 사용하면서, 최대 9배 빠른 속도를 보이며, 결과적으로 수 십억 규모의 그래프에서 랜덤 워크 기반의 개인화된 정점 랭킹을 성공적으로 구할 수 있다. 또한, 제안하는 랜덤 워크 기반의 모델들은 부호화된 네트워크와 지식 베이스와 같은 라벨이 있는 그래프에서 부호 예측, 간선 예측, 이상 현상 탐지, 관계 추론 등의 다양한 응용에서 다른 경쟁 모델들보다 더 좋은 예측 성능을 보인다.Chapter1 Overview .... 1 1.1 Motivation .... 1 1.2 Research Statement .... 4 1.2.1 Research Goals and Importance .... 4 1.2.2 Technical Challenges .... 6 1.2.3 Main Approaches .... 7 1.2.4 Contributions .... 9 1.2.5 Overall Impact .... 10 1.3 Thesis Organization .... 11 Chapter2 Background .... 12 2.1 Definitions .... 12 2.1.1 Notations on Graphs .... 12 2.1.2 Random Walk with Restart .... 13 2.2 Related Works .... 15 2.2.1 Previous Methods for RWR in Plain Graphs .... 15 2.2.2 Ranking Models in Signed Networks .... 17 2.2.3 Relational Reasoning Models in Edge-labeled Graphs .... 19 Chapter 3 Fast and Scalable Ranking in Large-scale Plain Graphs .... 21 3.1 Introduction .... 21 3.2 Preliminaries .... 23 3.2.1 Iterative Methods for RWR .... 24 3.2.2 Preprocessing Methods for RWR .... 25 3.3 Proposed Method .... 26 3.3.1 Overview .... 26 3.3.2 BePI-B: Exploiting Graph Characteristics for Node Reordering and Block Elimination .... 28 3.3.3 BePI-B: Incorporating an Iterative Method into Block Elimination .... 32 3.3.4 BePI-S: Sparsifying the Schur Complement .... 34 3.3.5 BePI: Preconditioning a Linear System for the Iterative Method .... 36 3.4 Theoretical Results .... 39 3.4.1 Time Complexity .... 39 3.4.2 Space Complexity .... 40 3.4.3 Accuracy Bound .... 41 3.4.4 Lemmas and Proofs .... 43 3.5 Experiments .... 48 3.5.1 Experimental Settings .... 49 3.5.2 Preprocessing Cost .... 51 3.5.3 Query Cost .... 53 3.5.4 Scalability .... 53 3.5.5 Effects of Sparse Schur Complement and Preconditioning .... 54 3.5.6 Effects of the Hub Selection Ratio .... 57 3.5.7 Accuracy .... 58 3.5.8 Comparison with the-State-of-the-Art Method .... 59 3.6 Summary .... 60 Chapter 4 Personalized Ranking in Signed Graphs .... 61 4.1 Introduction .... 61 4.2 Problem Definition .... 65 4.3 Proposed Method .... 65 4.3.1 Signed Random Walk with Restart Model .... 66 4.3.2 SRWR-Iter: Iterative Algorithm for Signed Random Walk with Restart .... 76 4.3.3 SRWR-Pre: Preprocessing Algorithm for Signed Random Walk with Restart .... 82 4.4 Experiments .... 93 4.4.1 Experimental Settings .... 94 4.4.2 Link Prediction Task .... 96 4.4.3 User Preference Preservation Task .... 99 4.4.4 Troll Identification Task .... 100 4.4.5 Sign Prediction Task .... 104 4.4.6 Effectiveness of Balance Attenuation Factors .... 109 4.4.7 Performance of SRWR-Pre .... 110 4.5 Summary .... 113 Chapter 5 Relational Reasoning in Edge-labeled Graphs .... 114 5.1 Introduction .... 114 5.2 Preliminary .... 116 5.3 Proposed Method .... 118 5.3.1 Label Transition Observation .... 120 5.3.2 Learning Label Transition Probabilities .... 121 5.3.3 Multi-Labeled Random Walk with Restart .... 123 5.3.4 Formulation for MuRWR .... 125 5.3.5 Algorithm for MuRWR .... 127 5.4 Theoretical Results .... 131 5.4.1 Lemma for Solution of Label Transition Probabilities and Convexity .... 131 5.4.2 Lemma for Recursive Equation of MuRWR Score Matrix .... 134 5.4.3 Lemma for Spectral Radius in Convergence Theorem .... 136 5.4.4 Lemma for Complexity Analysis .... 137 5.5 Experiment .... 138 5.5.1 Experimental Settings .... 139 5.5.2 Relation Inference Task .... 140 5.5.3 Effects of Label Weights in MuRWR .... 142 5.5.4 Effects of Restart Probability in MuRWR .... 143 5.5.5 Convergence of MuRWR .... 144 5.6 Summary .... 145 Chapter6 Future Works .... 146 6.1 Fast and Accurate Pseudoinverse Computation .... 146 6.2 Fast and Scalable Signed Network Generation .... 147 6.3 Disk-based Algorithms for Random Walk .... 147 Chapter7 Conclusion .... 149 References .... 151 Appendix .... 166 A.1 Hub-and-Spoke Reordering Method .... 166 A.2 Time Complexity of Sparse Matrix Multiplication .... 167 A.3 Details of Preconditioned GMRES .... 167 A.4 Detailed Description of Evaluation Metrics .... 170 A.4.1 Link Prediction .... 170 A.4.2 Troll Identification .... 171 A.5 Discussion on Relative Trustworthiness of SRWR .... 173 Abstract in Korean .... 176Docto

SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection

Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and being not scalable. In this work, we aim to overcome these drawbacks. Towards this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.Comment: 12 page

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Trust-based recommendation systems: an axiomatic approach

ABSTRACT High-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of social network structures, the recommendations may incorporate this information. This paper focuses on networks which represent trust and recommendations which incorporate trust relationships. The goal of a trust-based recommendation system is to generate personalized recommendations from known opinions and trust relationships. In analogy to prior work on voting and ranking systems, we use the axiomatic approach from the theory of social choice. We develop an natural set of five axioms which we desire any recommendation system exhibit. Then we show that no system can simultaneously satisfy all these axioms. We also exhibit systems which satisfy any four of the five axioms. Next we consider ways of weakening the axioms, which can lead to a unique recommendation system based on random walks. We consider other recommendation systems (personal page rank, majority of majorities, and min cut) and search for alternative axiomatizations which uniquely characterize these systems. Finally, we determine which of these systems are incentive compatible. This is an important property for systems deployed in a monetized environment: groups of agents interested in manipulating recommendations to make others share their opinion have nothing to gain from lying about their votes or their trust links