02118nas a2200229   4500000000100000000000100001008004100002260001200043653001000055653001600065653003400081100001500115700001700130700001700147700001900164245004600183856008000229300001000309490000600319520154900325022001401874       2020                            d  c12/202010aGames10aGame Theory10aRumour Source Detection (RSD)1 aMinni Jain1 aAman Jaswani1 aAnkita Mehra1 aLaqshay Mudgal00aRumour Source Detection Using Game Theory  uhttps://www.ijimai.org/journal/sites/default/files/2020-11/ijimai_6_4_5.pdf  a49-560 v63 aSocial networks have become a critical part of our lives as they enable us to interact with a lot of people. These networks have become the main sources for creating, sharing and also extracting information regarding various subjects. But all this information may not be true and may contain a lot of unverified rumours that have the potential of spreading incorrect information to the masses, which may even lead to situations of widespread panic. Thus, it is of great importance to identify those nodes and edges that play a crucial role in a network in order to find the most influential sources of rumour spreading. Generally, the basic idea is to classify the nodes and edges in a network with the highest criticality. Most of the existing work regarding the same focuses on using simple centrality measures which focus on the individual contribution of a node in a network. Game-theoretic approaches such as Shapley Value (SV) algorithms suggest that individual marginal contribution should be measured for a given player as the weighted average marginal increase in the yield of any coalition that this player might join. For our experiment, we have played five SV-based games to find the top 10 most influential nodes on three network datasets (Enron, USAir97 and Les Misérables). We have compared our results to the ones obtained by using primitive centrality measures. Our results show that SVbased approach is better at understanding the marginal contribution, and therefore the actual influence, of each node to the entire network.  a1989-1660