Uncovering Active Communities from Directed Graphs on Distributed Spark Frameworks, Case Study: Twitter Data

• Published in: Big data and cognitive computing Vol. 5; no. 4; p. 46
• Main Authors: Moertini, Veronica S., Adithia, Mariskha T.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Algorithms; Apexes; Big Data; Case studies; Data analysis; Data processing; Data science; Datasets; directed graphs analysis; graph data mining on Spark; Graph theory; Graphs; off-line data stream analysis; scalable communities detection; social network analysis; Social networks
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc5040046

Directed graphs can be prepared from big data containing peoples’ interaction information. In these graphs the vertices represent people, while the directed edges denote the interactions among them. The number of interactions at certain intervals can be included as the edges’ attribute. Thus, the larger the count, the more frequent the people (vertices) interact with each other. Subgraphs which have a count larger than a threshold value can be created from these graphs, and temporal active communities can then be mined from each of these subgraphs. Apache Spark has been recognized as a data processing framework that is fast and scalable for processing big data. It provides DataFrames, GraphFrames, and GraphX APIs which can be employed for analyzing big graphs. We propose three kinds of active communities, namely, Similar interest communities (SIC), Strong-interacting communities (SC), and Strong-interacting communities with their “inner circle” neighbors (SCIC), along with algorithms needed to uncover them. The algorithm design and implementation are based on these APIs. We conducted experiments on a Spark cluster using ten machines. The results show that our proposed algorithms are able to uncover active communities from public big graphs as well from Twitter data collected using Spark structured streaming. In some cases, the execution time of the algorithms that are based on GraphFrames’ motif findings is faster.