1,420 research outputs found
Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees
Given a time-evolving graph, how can we track similarity between nodes in a
fast and accurate way, with theoretical guarantees on the convergence and the
error? Random Walk with Restart (RWR) is a popular measure to estimate the
similarity between nodes and has been exploited in numerous applications. Many
real-world graphs are dynamic with frequent insertion/deletion of edges; thus,
tracking RWR scores on dynamic graphs in an efficient way has aroused much
interest among data mining researchers. Recently, dynamic RWR models based on
the propagation of scores across a given graph have been proposed, and have
succeeded in outperforming previous other approaches to compute RWR
dynamically. However, those models fail to guarantee exactness and convergence
time for updating RWR in a generalized form. In this paper, we propose OSP, a
fast and accurate algorithm for computing dynamic RWR with insertion/deletion
of nodes/edges in a directed/undirected graph. When the graph is updated, OSP
first calculates offset scores around the modified edges, propagates the offset
scores across the updated graph, and then merges them with the current RWR
scores to get updated RWR scores. We prove the exactness of OSP and introduce
OSP-T, a version of OSP which regulates a trade-off between accuracy and
computation time by using error tolerance {\epsilon}. Given restart probability
c, OSP-T guarantees to return RWR scores with O ({\epsilon} /c ) error in O
(log ({\epsilon}/2)/log(1-c)) iterations. Through extensive experiments, we
show that OSP tracks RWR exactly up to 4605x faster than existing static RWR
method on dynamic graphs, and OSP-T requires up to 15x less time with 730x
lower L1 norm error and 3.3x lower rank error than other state-of-the-art
dynamic RWR methods.Comment: 10 pages, 8 figure
ν° κ·Έλν μμμμ κ°μΈνλ νμ΄μ§ λν¬μ λν λΉ λ₯Έ κ³μ° κΈ°λ²
νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2020. 8. μ΄μꡬ.Computation of Personalized PageRank (PPR) in graphs is an important function that is widely utilized in myriad application domains such as search, recommendation, and knowledge discovery. Because the computation of PPR is an expensive process, a good number of innovative and efficient algorithms for computing PPR have been developed. However, efficient computation of PPR within very large graphs with over millions of nodes is still an open problem. Moreover, previously proposed algorithms cannot handle updates efficiently, thus, severely limiting their capability of handling dynamic graphs. In this paper, we present a fast converging algorithm that guarantees high and controlled precision. We improve the convergence rate of traditional Power Iteration method by adopting successive over-relaxation, and initial guess revision, a vector reuse strategy. The proposed method vastly improves on the traditional Power Iteration in terms of convergence rate and computation time, while retaining its simplicity and strictness. Since it can reuse the previously computed vectors for refreshing PPR vectors, its update performance is also greatly enhanced. Also, since the algorithm halts as soon as it reaches a given error threshold, we can flexibly control the trade-off between accuracy and time, a feature lacking in both sampling-based approximation methods and fully exact methods. Experiments show that the proposed algorithm is at least 20 times faster than the Power Iteration and outperforms other state-of-the-art algorithms.κ·Έλν
λ΄μμ κ°μΈνλ νμ΄μ§λν¬ (P ersonalized P age R ank, PPR λ₯Ό κ³μ°νλ κ²μ κ²μ , μΆμ² , μ§μλ°κ²¬ λ± μ¬λ¬ λΆμΌμμ κ΄λ²μνκ² νμ©λλ μ€μν μμ
μ΄λ€ . κ°μΈνλ νμ΄μ§λν¬λ₯Ό κ³μ°νλ κ²μ κ³ λΉμ©μ κ³Όμ μ΄ νμνλ―λ‘ , κ°μΈνλ νμ΄μ§λν¬λ₯Ό κ³μ°νλ ν¨μ¨μ μ΄κ³ νμ μ μΈ λ°©λ²λ€μ΄ λ€μ κ°λ°λμ΄μλ€ . κ·Έλ¬λ μλ°±λ§ μ΄μμ λ
Έλλ₯Ό κ°μ§ λμ©λ κ·Έλνμ λν ν¨μ¨μ μΈ κ³μ°μ μ¬μ ν ν΄κ²°λμ§ μμ λ¬Έμ μ΄λ€ . κ·Έμ λνμ¬ , κΈ°μ‘΄ μ μλ μκ³ λ¦¬λ¬λ€μ κ·Έλν κ°±μ μ ν¨μ¨μ μΌλ‘ λ€λ£¨μ§ λͺ»νμ¬ λμ μΌλ‘ λ³ννλ κ·Έλνλ₯Ό λ€λ£¨λ λ°μ νκ³μ μ΄ ν¬λ€ . λ³Έ μ°κ΅¬μμλ λμ μ λ°λλ₯Ό 보μ₯νκ³ μ λ°λλ₯Ό ν΅μ κ°λ₯ν , λΉ λ₯΄κ² μλ ΄νλ κ°μΈνλ νμ΄μ§λν¬ κ³μ° μκ³ λ¦¬λ¬μ μ μνλ€ . μ ν΅μ μΈ κ±°λμ κ³±λ² (Power μ μΆμ°¨κ°μμνλ² (Successive Over Relaxation) κ³Ό μ΄κΈ° μΆμΈ‘ κ° λ³΄μ λ² (Initial Guess μ νμ©ν λ²‘ν° μ¬μ¬μ© μ λ΅μ μ μ©νμ¬ μλ ΄ μλλ₯Ό κ°μ νμλ€ . μ μλ λ°©λ²μ κΈ°μ‘΄ κ±°λμ κ³±λ²μ μ₯μ μΈ λ¨μμ±κ³Ό μλ°μ±μ μ μ§ νλ©΄μ λ μλ ΄μ¨κ³Ό κ³μ°μλλ₯Ό ν¬κ² κ°μ νλ€ . λν κ°μΈνλ νμ΄μ§λν¬ λ²‘ν°μ κ°±μ μ μνμ¬ μ΄μ μ κ³μ° λμ΄ μ μ₯λ 벑ν°λ₯Ό μ¬μ¬μ©ν μ¬ , κ°±μ μ λλ μκ°μ΄ ν¬κ² λ¨μΆλλ€ . λ³Έ λ°©λ²μ μ£Όμ΄μ§ μ€μ°¨ νκ³μ λλ¬νλ μ¦μ κ²°κ³Όκ°μ μ°μΆνλ―λ‘ μ νλμ κ³μ°μκ°μ μ μ°νκ² μ‘°μ ν μ μμΌλ©° μ΄λ νλ³Έ κΈ°λ° μΆμ λ°©λ²μ΄λ μ νν κ°μ μ°μΆνλ μνλ ¬ κΈ°λ° λ°©λ² μ΄ κ°μ§μ§ λͺ»ν νΉμ±μ΄λ€ . μ€ν κ²°κ³Ό , λ³Έ λ°©λ²μ κ±°λμ κ³±λ²μ λΉνμ¬ 20 λ°° μ΄μ λΉ λ₯΄κ² μλ ΄νλ€λ κ²μ΄ νμΈλμμΌλ©° , κΈ° μ μλ μ΅κ³ μ±λ₯ μ μκ³ λ¦¬ λ¬ λ³΄λ€ μ°μν μ±λ₯μ 보μ΄λ κ² λν νμΈλμλ€1 Introduction 1
2 Preliminaries: Personalized PageRank 4
2.1 Random Walk, PageRank, and Personalized PageRank. 5
2.1.1 Basics on Random Walk 5
2.1.2 PageRank. 6
2.1.3 Personalized PageRank 8
2.2 Characteristics of Personalized PageRank. 9
2.3 Applications of Personalized PageRank. 12
2.4 Previous Work on Personalized PageRank Computation. 17
2.4.1 Basic Algorithms 17
2.4.2 Enhanced Power Iteration 18
2.4.3 Bookmark Coloring Algorithm. 20
2.4.4 Dynamic Programming 21
2.4.5 Monte-Carlo Sampling. 22
2.4.6 Enhanced Direct Solving 24
2.5 Summary 26
3 Personalized PageRank Computation with Initial Guess Revision 30
3.1 Initial Guess Revision and Relaxation 30
3.2 Finding Optimal Weight of Successive Over Relaxation for PPR. 34
3.3 Initial Guess Construction Algorithm for Personalized PageRank. 36
4 Fully Personalized PageRank Algorithm with Initial Guess Revision 42
4.1 FPPR with IGR. 42
4.2 Optimization. 49
4.3 Experiments. 52
5 Personalized PageRank Query Processing with Initial Guess Revision 56
5.1 PPR Query Processing with IGR 56
5.2 Optimization. 64
5.3 Experiments. 67
6 Conclusion 74
Bibliography 77
Appendix 88
Abstract (In Korean) 90Docto
Non-Conservative Diffusion and its Application to Social Network Analysis
The random walk is fundamental to modeling dynamic processes on networks.
Metrics based on the random walk have been used in many applications from image
processing to Web page ranking. However, how appropriate are random walks to
modeling and analyzing social networks? We argue that unlike a random walk,
which conserves the quantity diffusing on a network, many interesting social
phenomena, such as the spread of information or disease on a social network,
are fundamentally non-conservative. When an individual infects her neighbor
with a virus, the total amount of infection increases. We classify diffusion
processes as conservative and non-conservative and show how these differences
impact the choice of metrics used for network analysis, as well as our
understanding of network structure and behavior. We show that Alpha-Centrality,
which mathematically describes non-conservative diffusion, leads to new
insights into the behavior of spreading processes on networks. We give a
scalable approximate algorithm for computing the Alpha-Centrality in a massive
graph. We validate our approach on real-world online social networks of Digg.
We show that a non-conservative metric, such as Alpha-Centrality, produces
better agreement with empirical measure of influence than conservative metrics,
such as PageRank. We hope that our investigation will inspire further
exploration into the realms of conservative and non-conservative metrics in
social network analysis
Fast Exact CoSimRank Search on Evolving and Static Graphs
In real Web applications, CoSimRank has been proposed as a powerful measure of node-pair similarity based on graph topologies. However, existing work on CoSimRank is restricted to static graphs. When the graph is updated with new edges arriving over time, it is cost-inhibitive to recompute all CoSimRank scores from scratch, which is impractical. In this study, we propose a fast dynamic scheme, \DCoSim for accurate CoSimRank search over evolving graphs. Based on \DCoSim, we also propose a fast scheme, \FCoSim, that greatly accelerates CoSimRank search over static graphs. Our theoretical analysis shows that \DCoSim and \FCoSim guarantee the exactness of CoSimRank scores. On the static graph G, to efficiently retrieve CoSimRank scores \mathbfS , \FCoSim is based on three ideas: (i) It first finds a "spanning polytree»» T over G. (ii) On T, a fast algorithm is designed to compute the CoSimRank scores \mathbfS (T) over the "spanning polytree»» T. (iii) On G, \DCoSim is employed to compute the changes of \mathbfS (T) in response to the delta graph . Experimental evaluations verify the superiority of \DCoSim over evolving graphs, and the fast speedup of \FCoSim on large-scale static graphs against its competitors, without any loss of accuracy
μ€μΈκ³ κ·Έλν νΉμ§μ νμ©ν λλ€ μν¬ κΈ°λ° λκ·λͺ¨ κ·Έλν λ§μ΄λ
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν μ»΄ν¨ν°κ³΅νλΆ,2020. 2. κ°μ .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.λ€μν μ€μΈκ³ μμ° νμμμμ κ΄κ³λ€μ μμ
λ€νΈμν¬, νμ΄νΌλ§ν¬ λ€νΈμν¬μ λ¨λ°±μ§ μνΈμμ© λ€νΈμν¬μ κ°μ΄ μ μ κ³Ό κ°μμ κ·Έλνλ‘ ννλλ€. μ΄λ¬ν λ€νΈμν¬λ₯Ό λΆμνλ κ²μ μ€μΈκ³μ νμμ μ΄ν΄νλλ° λ§€μ° μ€μνλ€. λ€μν κ·Έλν λΆμ κΈ°λ²μ€μ λλ€ μν¬λΌλ κΈ°λ²μ΄ λ§μ‘±μ€λ¬μ΄ μ±λ₯κ³Ό ν¨κ» λ§μ κ·Έλν λ§μ΄λ μμ©μ λ리 νμ©λμ΄ μλ€. κ·Έλ¬λ λλ€μμ μ€μΈκ³ κ·Έλνλ κ·Έ κ·λͺ¨κ° κ΅μ₯ν ν¬κ³ λ€μν λΌλ²¨ μ 보μ ν¨κ» 볡μ‘νκ² ννλλ€. μ ν΅μ μΈ λλ€ μν¬ κΈ°λ°μ κΈ°λ²λ€μ κ³μ°λμ΄ λ§μ΄ μꡬλκ³ , λλ€ μν¬λ₯Ό νλλ° μμ΄μ λ€μν λΌλ²¨ μ 보λ₯Ό μ ν κ³ λ €νμ§ μμ λΌλ²¨λ‘ ννλλ κ·Έλνμ κ³ μ ν νΉμ±μ΄ 무μλκ² λλ€. κ·Έλμ μ΄μ κ°μ΄ 볡μ‘νλ©΄μ λκ·λͺ¨ κ·Έλνμμλ λλ€ μν¬μ μ€μ§μ νμ©μ΄ μ νλμ΄μλ€.
λ³Έ νμ λ
Όλ¬Έμμλ λλ€ μν¬ κΈ°λ°μ λκ·λͺ¨ μ€μΈκ³ κ·Έλν λΆμμ κΈ°μ μ νκ³λ₯Ό ν΄κ²°νκ³ μ νλ€. μ€μΈκ³ κ·Έλνλ κ³ μ ν ꡬ쑰μ νΉμ§λ€μ κ°μ§κ³ μμΌλ©° μ΄λ¬ν ꡬ쑰μ νΉμ§λ€μ μλμ νμ§μ μΈ‘λ©΄μμ λλ€ μν¬μ μ±λ₯μ ν₯μμν€λλ° κΈ°λ°μ΄ λ μ μλ€. μ΄λ¬ν μμ΄λμ΄λ₯Ό νμ©νμ¬, λκ·λͺ¨μ λΌλ²¨μ΄ μλ μΌλ°μ μΈ λ€νΈμν¬μμ λλ€ μν¬ κΈ°λ°μ κ°μΈνλ μ μ λνΉ κ³μ°μ λΉ λ₯΄κ³ , νμ₯μ± μκ³ μ ννκ² κ΅¬νλ κΈ°λ²μ μ μνλ€. λν λΆνΈνλ λ€νΈμν¬ λλ μ§μ λ² μ΄μ€μ κ°μ λΌλ²¨μ΄ μλ κ·Έλνμμ κ°μΈνλ μ μ λνΉκ³Ό κ΄κ³ μΆλ‘ μ μν λλ€ μν¬ κΈ°λ°μ λͺ¨λΈμ μ μνλ€.
λ€μν μ€μΈκ³ κ·Έλνμμ κ΄λ²μν μ€νμ ν΅ν΄ λ³Έ νμ λ
Όλ¬Έμ μν΄ μ μλ λ°©λ²κ³Ό λͺ¨λΈμ ν¨κ³Όμ±μ 보μΈλ€. μ μνλ λ°©λ²μ λ€λ₯Έ κ²½μ κΈ°λ²λ€κ³Ό λΉκ΅νμ λ μ΅λ 100λ°° λ ν° κ·Έλνλ₯Ό μ²λ¦¬ν μ μκ³ , μ΅λ 130λ°° μ κ² λ©λͺ¨λ¦¬λ₯Ό μ¬μ©νλ©΄μ, μ΅λ 9λ°° λΉ λ₯Έ μλλ₯Ό 보μ΄λ©°, κ²°κ³Όμ μΌλ‘ μ μμ΅ κ·λͺ¨μ κ·Έλνμμ λλ€ μν¬ κΈ°λ°μ κ°μΈνλ μ μ λνΉμ μ±κ³΅μ μΌλ‘ ꡬν μ μλ€. λν, μ μνλ λλ€ μν¬ κΈ°λ°μ λͺ¨λΈλ€μ λΆνΈνλ λ€νΈμν¬μ μ§μ λ² μ΄μ€μ κ°μ λΌλ²¨μ΄ μλ κ·Έλνμμ λΆνΈ μμΈ‘, κ°μ μμΈ‘, μ΄μ νμ νμ§, κ΄κ³ μΆλ‘ λ±μ λ€μν μμ©μμ λ€λ₯Έ κ²½μ λͺ¨λΈλ€λ³΄λ€ λ μ’μ μμΈ‘ μ±λ₯μ 보μΈλ€.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
Quick Detection of High-degree Entities in Large Directed Networks
In this paper, we address the problem of quick detection of high-degree
entities in large online social networks. Practical importance of this problem
is attested by a large number of companies that continuously collect and update
statistics about popular entities, usually using the degree of an entity as an
approximation of its popularity. We suggest a simple, efficient, and easy to
implement two-stage randomized algorithm that provides highly accurate
solutions for this problem. For instance, our algorithm needs only one thousand
API requests in order to find the top-100 most followed users in Twitter, a
network with approximately a billion of registered users, with more than 90%
precision. Our algorithm significantly outperforms existing methods and serves
many different purposes, such as finding the most popular users or the most
popular interest groups in social networks. An important contribution of this
work is the analysis of the proposed algorithm using Extreme Value Theory -- a
branch of probability that studies extreme events and properties of largest
order statistics in random samples. Using this theory, we derive an accurate
prediction for the algorithm's performance and show that the number of API
requests for finding the top-k most popular entities is sublinear in the number
of entities. Moreover, we formally show that the high variability among the
entities, expressed through heavy-tailed distributions, is the reason for the
algorithm's efficiency. We quantify this phenomenon in a rigorous mathematical
way
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