TY - GEN
T1 - Visualizing graph differences from social media streams
AU - Shin, Minjeong
AU - Kim, Dongwoo
AU - Lee, Jae Hee
AU - Bista, Umanga
AU - Xie, Lexing
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/1/30
Y1 - 2019/1/30
N2 - We propose KGdiff, a new interactive visualization tool for social media content focusing on entities and relationships. The core component is a layout algorithm that highlights the differences between two graphs. We apply this algorithm on knowledge graphs consisting of named entities and their relations extracted from text streams over different time periods. The visualization system provides additional information such as the volume and frequency ranking of entities and allows users to select which parts of the graph to visualize interactively. On Twitter and news article collections, KGdiff allows users to compare different data subsets. Results of such comparisons often reveal topical or geographical changes in a discussion. More broadly, graph differences are useful for a wide range of relational data comparison tasks, such as comparing social interaction graphs, identifying changes in user behavior, or discovering differences in graphs from distinct sources, geography, or political stance.
AB - We propose KGdiff, a new interactive visualization tool for social media content focusing on entities and relationships. The core component is a layout algorithm that highlights the differences between two graphs. We apply this algorithm on knowledge graphs consisting of named entities and their relations extracted from text streams over different time periods. The visualization system provides additional information such as the volume and frequency ranking of entities and allows users to select which parts of the graph to visualize interactively. On Twitter and news article collections, KGdiff allows users to compare different data subsets. Results of such comparisons often reveal topical or geographical changes in a discussion. More broadly, graph differences are useful for a wide range of relational data comparison tasks, such as comparing social interaction graphs, identifying changes in user behavior, or discovering differences in graphs from distinct sources, geography, or political stance.
KW - Graph diff
KW - Relation extraction
KW - Relational data comparison
UR - http://www.scopus.com/inward/record.url?scp=85061726053&partnerID=8YFLogxK
U2 - 10.1145/3289600.3290616
DO - 10.1145/3289600.3290616
M3 - Conference contribution
T3 - WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
SP - 806
EP - 809
BT - WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery (ACM)
T2 - 12th ACM International Conference on Web Search and Data Mining, WSDM 2019
Y2 - 11 February 2019 through 15 February 2019
ER -