16 research outputs found
Temporal motifs in patent opposition and collaboration networks
Patents are intellectual properties that reflect innovative activities of
companies and organizations. The literature is rich with the studies that
analyze the citations among the patents and the collaboration relations among
companies that own the patents. However, the adversarial relations between the
patent owners are not as well investigated. One proxy to model such relations
is the patent opposition, which is a legal activity in which a company
challenges the validity of a patent. Characterizing the patent oppositions,
collaborations, and the interplay between them can help better understand the
companies' business strategies. Temporality matters in this context as the
order and frequency of oppositions and collaborations characterize their
interplay. In this study, we construct a two-layer temporal network to model
the patent oppositions and collaborations among the companies. We utilize
temporal motifs to analyze the oppositions and collaborations from structural
and temporal perspectives. We first characterize the frequent motifs in patent
oppositions and investigate how often the companies of different sizes attack
other companies. We show that large companies tend to engage in opposition with
multiple companies. Then we analyze the temporal interplay between
collaborations and oppositions. We find that two adversarial companies are more
likely to collaborate in the future than two collaborating companies oppose
each other in the future
Incremental closeness centrality in distributed memory
Networks are commonly used to model traffic patterns, social interactions, or web pages. The vertices in a network do not possess the same characteristics: some vertices are naturally more connected and some vertices can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given vertex in the network. When the network is dynamic and keeps changing, the relative importance of the vertices also changes. The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose Streamer, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined, replicated parallelism, and SpMM-based BFSs, and it takes NUMA effects into account. It makes maintaining the Closeness Centrality values of real-life networks with millions of interactions significantly faster and obtains almost linear speedups on a 64 nodes 8 threads/node cluster
Graph manipulations for fast centrality computation
The betweenness and closeness metrics have always been intriguing and used in many analyses. Yet, they are expensive to compute. For that reason, making the betweenness and closeness centrality computations faster is an important and well-studied problem. In this work, we propose the framework, BADIOS, which manipulates the graph by compressing it and splitting into pieces so that the centrality computation can be handled independently for each piece. Although BADIOS is designed and fine-tuned for exact betweenness and closeness centrality, it can easily be adapted for approximate solutions as well. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types and sizes of networks. In particular, it reduces the betweenness centrality computation time of a 4.6 million edges graph from more than 5 days to less than 16 hours. For the same graph, we achieve to decrease the closeness computation time from more than 3 days to 6 hours (12.7x speedup)
Size-Aware Hypergraph Motifs
Complex systems frequently exhibit multi-way, rather than pairwise,
interactions. These group interactions cannot be faithfully modeled as
collections of pairwise interactions using graphs, and instead require
hypergraphs. However, methods that analyze hypergraphs directly, rather than
via lossy graph reductions, remain limited. Hypergraph motif mining holds
promise in this regard, as motif patterns serve as building blocks for larger
group interactions which are inexpressible by graphs. Recent work has focused
on categorizing and counting hypergraph motifs based on the existence of nodes
in hyperedge intersection regions. Here, we argue that the relative sizes of
hyperedge intersections within motifs contain varied and valuable information.
We propose a suite of efficient algorithms for finding triplets of hyperedges
based on optimizing the sizes of these intersection patterns. This formulation
uncovers interesting local patterns of interaction, finding hyperedge triplets
that either (1) are the least correlated with each other, (2) have the highest
pairwise but not groupwise correlation, or (3) are the most correlated with
each other. We formalize this as a combinatorial optimization problem and
design efficient algorithms based on filtering hyperedges. Our experimental
evaluation shows that the resulting hyperedge triplets yield insightful
information on real-world hypergraphs. Our approach is also orders of magnitude
faster than a naive baseline implementation
Scalable Hybrid Implementation of Graph Coloring using MPI and OpenMP
Abstract—Graph coloring algorithms are commonly used in large scientific parallel computing either for identifying parallelism or as a tool to reduce computation, such as compressing Hessian matrices. Large scientific computations are nowadays either run on commodity clusters or on large computing platforms. In both cases, the current target platform is hierarchical with distributed memory at the node level and shared memory at the processor level. In this paper, we present a novel hybrid graph coloring algorithm and discuss how to obtain the best performance on such systems from algorithmic, system and engineering perspectives
Graph manipulations for fast centrality computation
The betweenness and closeness metrics are widely used metrics in many network analysis applications. Yet, they are expensive to compute. For that reason, making the betweenness and closeness centrality computations faster is an important and well-studied problem. In this work, we propose the framework BADIOS that manipulates the graph by compressing it and splitting into pieces so that the centrality computation can be handled independently for each piece. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types and sizes of networks. In particular, it reduces the betweenness centrality computation time of a 4.6 million edges graph from more than 5 days to less than 16 hours. For the same graph, the closeness computation time is decreased from more than 3 days to 6 hours (12.7x speedup)