URL: MESH.CS.UMN.EDU e3e1 Printingmesh.cs.umn.edu/posters/mesh-OpenHouse_Nov15.pdf ·...
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MESH : Enabling Scalable Social Group Analytics Via Hyper Graph Analysis Systems MOTIVATION Leveraging existing partitioning methods: Vertex cut Hyperedge cut Edge cut – 1D/2D Partitioning for load balancing. Sources of imbalance in hypergraph: • Dataset – Ratio of vertices to hyperedges • Skewed degree distribution –vertex or hyperedge or both • Computation – vertex Vs hyperedge program • Interaction – between vertices and hyperedges MESH : PROTOTYPE CHALLENGES: PARTITIONING CHALLENGES: REPRESENTATION Rapid growth of social data: Likes. Tweets, Publications Need to transform data into knowledge • Importance / centrality / influence • Community detection, Shortest paths • Information flow State of the art: Graph Computational Systems • Pregel, GraphLab (Dato), Apache Spark GraphX Students: Benjamin Heintz, Janani Thirunavukkarasu, Jayapriya Surendran, Shivangi Singh PIs: Abhishek Chandra, Jaideep Srivastava (University Of Minnesota, Twin Cities) URL: MESH.CS.UMN.EDU Sponsor : National Science Foundation To better model social group structure and behavior, we need hypergraph computing systems. v1 v2 v3 hyperedge vertex v1 v2 v3 v1 v2 v3 v1 v2 v3 e1 e2 e3 e1 e2 e3 v2 v1 v3 v2 v2 Multigraph Limited system support Hard to implement with existing APIs Can exploit differences between vertices, hyperedges Bipartite graph Obscures differences between hyperedges, vertices Portable to any graph system Dataset Vertices Hyperedges Bipartite Edges 1-mode Projection Edges DBLP 952,115 authors 916,947 collaborations 2,768,930 21,592,883 Friendster 7,944,949 users 1,620,991 communities 23,479,217 > 15.1 B Proof-of-concept prototype • Implemented on Apache Spark GraphX 1.2.1 • Run on shared 6-node cluster (2x6-core, 24GB RAM each) • Using bipartite graph representation Performance heavily affected by • Dataset Characteristics + Algorithm Characteristics • Representation + Partitioning 0 300 600 900 0 0.5 1 1.5 2 2.5 3 Execution Time (s) Number of Bipartite Edges (millions) Hypergraph PageRank: DBLP PR PR-Entropy 0 1000 2000 3000 0 5 10 15 20 Execution Time (s) Number of Bipartite Edges (millions) Hypergraph PageRank: Friendster PR PR-Entropy 0 100 200 300 400 500 600 700 PR PR-Entropy Execution Time (s) Partitioning: DBLP Cut Vertices Cut Hyperedges Cut Both Scalability is a real challenge. Iterative computation Vertex and hyperedge programs Message flow: • vertex hyperedge • hyperedge vertex RESULTS 0 500 1000 1500 2000 2500 3000 3500 4000 4500 PR CC SP Execution Time (s) Algorithms Algorithm Comparison Friendster DBLP
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MESH : Enabling Scalable Social Group Analytics Via Hyper Graph Analysis Systems
MOTIVATION
Leveraging existing partitioning methods:
Vertex cut Hyperedge cut Edge cut – 1D/2D
Partitioning for load balancing.
Sources of imbalance in hypergraph:
• Dataset – Ratio of vertices to hyperedges
• Skewed degree distribution –vertex or hyperedge or both
• Computation – vertex Vs hyperedge program
• Interaction – between vertices and hyperedges
MESH : PROTOTYPE CHALLENGES: PARTITIONING
CHALLENGES: REPRESENTATION
Rapid growth of social data: Likes. Tweets, Publications
Need to transform data into knowledge
• Importance / centrality / influence
• Community detection, Shortest paths
• Information flow
State of the art: Graph Computational Systems
• Pregel, GraphLab (Dato), Apache Spark GraphX
Students: Benjamin Heintz, Janani Thirunavukkarasu, Jayapriya Surendran, Shivangi Singh PIs: Abhishek Chandra, Jaideep Srivastava (University Of Minnesota, Twin Cities)
URL: MESH.CS.UMN.EDU Sponsor : National Science Foundation
To better model social group structure and behavior, we need hypergraph computing systems.
v1 v2
v3
hyperedge vertex
v1 v2
v3
v1 v2
v3
v1
v2
v3
e1
e2
e3
e1
e2
e3
v2v1
v3v2
v2
Multigraph Limited system support Hard to implement with existing APIs Can exploit differences between vertices, hyperedges
Bipartite graph Obscures differences between hyperedges, vertices Portable to any graph system
Dataset Vertices Hyperedges Bipartite
Edges 1-mode
Projection Edges
DBLP 952,115 authors
916,947 collaborations
2,768,930 21,592,883
Friendster 7,944,949
users 1,620,991
communities 23,479,217 > 15.1 B
Proof-of-concept prototype • Implemented on Apache Spark GraphX 1.2.1 • Run on shared 6-node cluster (2x6-core, 24GB RAM each) • Using bipartite graph representation
Performance heavily affected by • Dataset Characteristics + Algorithm Characteristics • Representation + Partitioning
0
300
600
900
0 0.5 1 1.5 2 2.5 3
Exe
cuti
on
Tim
e (
s)
Number of Bipartite Edges (millions)
Hypergraph PageRank: DBLP
PR PR-Entropy
0
1000
2000
3000
0 5 10 15 20
Exe
cuti
on
Tim
e (
s)
Number of Bipartite Edges (millions)
Hypergraph PageRank: Friendster
PR PR-Entropy
0
100
200
300
400
500
600
700
PR PR-Entropy
Exe
cuti
on
Tim
e (
s)
Partitioning: DBLP
Cut Vertices Cut Hyperedges Cut Both
Scalability is a real challenge.
Iterative computation
Vertex and hyperedge programs
Message flow: • vertex hyperedge • hyperedge vertex
RESULTS
0
500
1000
1500
2000
2500
3000
3500
4000
4500
PR CC SP
Exe
cuti
on
Tim
e (
s)
Algorithms
Algorithm Comparison
Friendster DBLP
