# PowerPoint 演示文稿 - PKU Efficient Cohesive Subgraph Detection in Parallel Yingxia Shao Lei Chen Bin Cui School of EECS, Peking University Hong Kong University of Science and Technology 1 Outline Background Preliminaries PETA: parallel and efficient truss detection algorithm Basic framework Triangle complete subgraph Subgraph-oriented model Optimization techniques Evaluation Conclusion 2

Cohesive Subgraph Cohesive subgraph identifies cohesive subgroups within social networks. helps social network analysts focus on areas of the network that are likely to be fruitful. E.g., clique, -clique, -clan, -club, plex, -core, etc. Image source: Large Scale Cohesive Subgraph Discovery for Social Network Visual Analysis, VLDB13 3 -Truss l -truss is a cohesive subgraph, where the P1 support of each edge is at least -2. Support of an edge is the number of triangles that contain the edge. The maximal -truss. k P2 b c Problem Statement:

Given a graph and a threshold , finding the maximal -truss in . o m P3 j a d h i e g f The subgraph with black thick edges is a 4-truss. 4 Fundamental operation The operation computes the support of an edge. Counting triangles around an edge . Two solutions Classic solution [Cohen 08, VLDB 13] 1.

2. Sorts the neighbors of each vertex in ascending order; Counts triangles in time complexity. Index-based solution [Wang 12] 1. When processing an edge , only enumerate neighbors of vertex which has smaller degree; 2. Test the existence of the third edge ( with the help of a HashTable. Time complexity: 5 A straightforward detection framework The framework is introduced by J. Cohen. 1. Enumerate triangles 2. For each edge, record the number of triangles, containing that edge 3. Keep only edges with the support greater than 4. If step 3 dropped any edges, return to step 1 5. The remained graph is the maximal -truss Inefficiency Mapreduce solution [J. Cohen 09] Two MapReduce jobs One is for steps 1-2.

One is for step 3. Pregel-based solution [L. Quick 12] Three supersteps. Two are for steps 1-2 One is for step 3. Classic solution of the fundamental operation. high communication cost large number of iterations 6 Contributions We propose a parallel and efficient truss detection algorithm. We introduce a subgraph-oriented programming model to efficiently implement the algorithm into popular graph computation systems. We address the edge-support law in real-world graph. 7 PETA: Parallel and Efficient Truss detection Algorithm New detection framework behind PETA 1. 2. 3. 4. Each worker constructs a special-designed subgraph; Simultaneously detects local -truss among workers;

Communicates the update when it is unavoidable; Goto step 2 until all local -trusses are stable. l l n P1 P2 b c m a j a d m k P3 h i

P2 k c m m a 3 i d d e P3 i m k P2 b

d 2 f g (b) special subgraphs New detection framework behind PETA c j d 2 e m i h i 3 f

(a) original graph g k j 2 f d i k l b e d m k n P1

o k o 1 P1 h i f 2 e P3 3 e f g (c) local -trusses 8 Triangle Complete Subgraph Cross Edge

Triangle Complete Subgraph (TC-subgraph) For internal and cross edges, TC-subgraph maintains all their triangles at local. Property External Edge k l P2 b a i d TC-subgraph preserves sufficient knowledge. Theorem 1 and Theorem 2 prove the correctness of new framework in PETA with TC-subgraph. m c e f

Internal Edge 9 Subgraph-Oriented Model The subgraph-oriented model allows to flexibly process the local subgraph by designing proper APIs. Vertex-centric Model Subgraph-oriented Model Accessible data Vertex and one-hop neighbors Entire local subgraph Access pattern Sequence Sequence/random Local updates Require extra supersteps By-pass User defined function Simple

Fruitful Expressivity Good Better In PETA, we can use index-based approach to detect local k-truss. *Refer to the paper for API design. 10 Local subgraph algorithm for PETA The algorithm contains two phases. Initialization phase. Constructs TC-subgraph via triangle counting routine Require two supersteps Detection phase. Apply index-based solution to compute the support of an edge. First detection iteration, scan over internal and cross edges. Successive detection iterations, modify local k-truss based on the removal of external edges. seamless detection ! 11 Efficiency analysis of PETA Computation Complexity

It is the same as the one of best-known serial algorithms, . Communication Complexity Worst case is bounded by 3||.||.. The number of iteration It is minimal when a graph partition is given. Space complexity Worst case is bounded by . Drawback The worst space cost is achievable in theoretic, thus it may be infeasible for large scale graphs. e.g., clique 12 Optimizations - I Edge replicating factor () Cross Edge is the average number that an edge is replicated. Small leads to low space cost, computation cost and communication cost. Small implies few number of iterations. External Edge k l

P2 b m a i d c e f Internal Edge is edge cut ratio and stands for cross edge replication. The third term is external edge replication, where is the support of an edge. 13 Optimizations - II Edge-Support Law in real-world graph The frequency distribution of the initial support of edges follows Power-Law. 14

Optimizations - III Edge-balanced partition strategy Improve the performance of the algorithm further. Use METIS to generate a good partition. Since METIS is unable to balance the core edges directly, we assign each vertexs degree as its weights, and balance the degree as an indicator for core edge balance. Graph E[(e)](e)]e)]] est rand metis livejournal 20.00 20.74 8.99 1.77 us-patent 2.36 3.25

3.13 1.19 wikitalk 5.93 7.53 4.52 3.31 dbpedia 7.61 9.11 6.77 2.10 Graphs are partitioned into 32 parts. 15 Evaluation All experiments are conducted on a cluster with 23 physical nodes. Graph |V|

|E| wikitalk 2.4M 4.7M us-patent 3.8M 16.5M livejournal 4.8M 42.9M dbpedia 17.2M 117.4M Baselines Cohen-MR [J. Cohen 09] Orig-LQ [L. Quick 12] 16

Efficiency livejournal us-patent On different datasets, PETA achieves 5x to 6x speedup compared with original pregel-based solution (i.e., origLQ and impr-LQ). The performance of Cohen-MR is at least 10X slower than the best one. So, it is not visualized for figures clarity. 17 Scalability 10-truss in dbpedia 40-truss in dbpedia The performance of PETA improves gracefully on both random and METIS-based partition schemes. 18 Conclusions We designed an efficient parallel -truss detection algorithm, named PETA and thoroughly prove the advantages of PETA. The subgraph-oriented model has potential to improve the performance of other complex graph analysis tasks. In future, we will solve other truss-related problem under this framework. 19 Q&A 20

Backup Expr. the number of iterations K-truss Orig-LQ Cohen-MR PETA Random METIS 5-truss 2212(503) 1006(503) 21 9 10-truss 272(68) 136(68) 23 14

40-truss 112(28) 56(28) 14 6 The number of iterations for k-truss detection on dbpedia 21