| dc.description.abstract | Understanding and maximizing the information’s dynamics flow in social networks
requires consideration of influence maximization. Traditional influence maximizing
techniques, on the other hand, tend to emphasize good associations and downplay the
significance of negative ones, which limits their application in real-world situations
where friend or foe interactions coexist. This thesis provides "Signed-PageRank," a fresh
and effective framework made to solve impact maximization in signed social networks,
as a solution to this issue.
Utilizing the idea of signed edges, the Signed-PageRank architecture take into account
both favorable and unfavorable engagements between nodes. This thesis provides a
modified PageRank method that considers the impact of signed edges in order to
precisely represent the dynamics of influence inside the network. Our method offers a
more thorough understanding of patterns of influence propagation and the creation of
attitudes in complex social networks by including both the positive and negative
interactions.
In addition, this thesis provides a scalable technique to quickly calculate the SignedPageRank scores, allowing us to recognize significant nodes and their related impact
values in huge signed social networks. This thesis undertakes comprehensive tests on
real-world dataset of Slashdot to show the efficacy of our approach.
As a result, the Signed-PageRank architecture we've described represents a major
improvement in impact maximizing for social networks with signed edges. It brings us
new perspectives for comprehending information spread, sentiment propagation, and
behavioral dynamics in actual social systems by taking into account interactions
involving both favorable and unfavorable elements. | en_US |
