Influence maximization method and system based on group in social network

Abstract

The invention provides an influence maximization method and system based on a group in a social network, and the method comprises the steps: 1, mapping a node to a representation space through a random walk method in the social network, and keeping the influence propagation attribute of the node; 2, defining and calculating the propagation affinity between nodes, combining the adjacent node pairswith the highest propagation affinity in sequence until a set compression ratio is met so as to obtain a coarsened network, wherein each node corresponds to one group in the original network; and 3, constructing an influence propagation function of a candidate seed set according to attributes of propagation of the influence of the nodes in the groups and between the groups, and selecting a maximuminfluence user set containing a preset number of nodes according to a greedy algorithm. The method has higher time efficiency under a similar influence propagation effect, and has a better influencepropagation effect under the similar time efficiency.

Description

technical field [0001] The present invention relates to the technical field of social network influence maximization, in particular, to a method and system for maximizing influence based on groups in a social network. In particular, it relates to a method for group division and group-based influence maximization through node representation and clustering in a large-scale social network. Background technique [0002] Online Social Networks (OSNs) have become an important platform for the rapid dissemination of information and influence among a large number of user groups. The influence maximization problem aims to tap a group of seed users, and after a series of dissemination, make the final affected The number of users is the largest, and this research has important commercial value in precision marketing, opinion leader discovery, and public opinion management and control. [0003] After searching the existing literature at home and abroad, it is found that in view of the ...

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Problems solved by technology

[0004] The above-mentioned CSPIN algorithm reasonably compresses the network scale and maintains the influence propagation properties of the network, greatly reducing the complexity of point selection, but the accuracy of the algorithm depends on the selection algorithm of the seed group, which usually lacks accuracy guarantee, and the selection of seed nodes The method is simply to randomly select a node from each seed group, and lacks an approximate estimation method for node influence

The CoFIM algorithm can quickly estimate the spread influence of nodes, has a low time complexity and has a certain accuracy guarantee, but the community discovery based on connection density does not consider the attributes and properties of the network and nodes in the spread of influence, so Approximating the influence of nodes in the community as a constant value lacks theoretical support, and this method uses the same constant value to approximate the influence of nodes in each community, ignoring the important factor of community size

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