3 research outputs found
Balanced Butterfly Counting in Bipartite-Network
Bipartite graphs offer a powerful framework for modeling complex
relationships between two distinct types of vertices, incorporating
probabilistic, temporal, and rating-based information. While the research
community has extensively explored various types of bipartite relationships,
there has been a notable gap in studying Signed Bipartite Graphs, which capture
liking / disliking interactions in real-world networks such as
customer-rating-product and senator-vote-bill. Balance butterflies,
representing 2 x 2 bicliques, provide crucial insights into antagonistic
groups, balance theory, and fraud detection by leveraging the signed
information. However, such applications require counting balance butterflies
which remains unexplored. In this paper, we propose a new problem: counting
balance butterflies in a signed bipartite graph. To address this problem, we
adopt state-of-the-art algorithms for butterfly counting, establishing a smart
baseline that reduces the time complexity for solving our specific problem. We
further introduce a novel bucket approach specifically designed to count
balanced butterflies efficiently. We propose a parallelized version of the
bucketing approach to enhance performance. Extensive experimental studies on
nine real-world datasets demonstrate that our proposed bucket-based algorithm
is up to 120x faster over the baseline, and the parallel implementation of the
bucket-based algorithm is up to 45x faster over the single core execution.
Moreover, a real-world case study showcases the practical application and
relevance of counting balanced butterflies
Searching Personalized -wing in Large and Dynamic Bipartite Graphs
There are extensive studies focusing on the application scenario that all the
bipartite cohesive subgraphs need to be discovered in a bipartite graph.
However, we observe that, for some applications, one is interested in finding
bipartite cohesive subgraphs containing a specific vertex. In this paper, we
study a new query dependent bipartite cohesive subgraph search problem based on
-wing model, named as the personalized -wing search problem. We introduce
a -wing equivalence relationship to summarize the edges of a bipartite graph
into groups. Therefore, all the edges of are segregated into different
groups, i.e. -wing equivalence class, forming an efficient and wing number
conserving index called EquiWing. Further, we propose a more compact version of
EquiWing, EquiWing-Comp, which is achieved by integrating our proposed
-butterfly loose approach and discovered hierarchy properties. These indices
are used to expedite the personalized -wing search with a non-repetitive
access to , which leads to linear algorithms for searching the personalized
-wing. Moreover, we conduct a thorough study on the maintenance of the
proposed indices for evolving bipartite graphs. We discover novel properties
that help us localize the scope of the maintenance at a low cost. By exploiting
the discoveries, we propose novel algorithms for maintaining the two indices,
which substantially reduces the cost of maintenance. We perform extensive
experimental studies in real, large-scale graphs to validate the efficiency and
effectiveness of EquiWing and EquiWing-Comp compared to the baseline.Comment: 13 pages, 10 figures and 4 table