Name
Abstract | ||
|---|---|---|
BSTRACT Social networks are becoming increasingly a source of wealth for people to connect with others in the society and express themselves. These networks store huge amounts of data related to individual and collective behavior, and relationships. Despite their importance, there exists few research that explains the factors leading to the evolution of these relationships, as well as abrupt changes in the behavior of individuals in contact. This paper proposes an approach based on the topology of social networks to detect early warnings of such changes, called weak signals. Our approach is in contrast to existing works that focus on analyzing major themes and trends, i.e. strong signals, prevalent in a social network at a particular point in time. We rely on a temporal interaction graph, and extract patterns that characterize weak signals. We demonstrate our approach and validate the detected signals through the analysis of social interactions between individuals of a captive Guinea baboons group, and confirm the existence of weak signals prior to the occurrence of an aggressive behavior. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3548785.3548807 | International Database Engineering & Applications Symposium |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiba Abou Jamra | 1 | 0 | 0.34 |
| Marinette Savonnet | 2 | 0 | 0.34 |
| Eric Leclercq | 3 | 28 | 15.63 |