Triad link prediction method based on the evolutionary analysis with IoT in opportunistic social networks

Abstract The data volume of various smart devices has exploded with the advent of the 5G communication era and the rapid development of Internet of Things technology. A large amount of data has higher requirements on the transmission protocol in the network. Most of the existing research focuses on studying a single node as the basic information transmission unit. However, social networks have complex topological structures, and such methods are difficult to grasp the evolutionary rules of the network. It usually leads to higher resource waste and problem complexity. A triad (a network subgraph containing three nodes) can not only provide accurate local topology information, but also its conversion rules are easy to describe, which can reduce the complexity of the problem. The mutual conversion between triads constitutes the evolution of the entire network. Based on this, this article first proposes a triad-based social network evolution analysis method (SNEA). SNEA includes a prediction algorithm (TPMPA) that learns the evolution of the triad transition probability matrix through time series and a quantization algorithm (TTHQA) that quantifies the impact of triad transformations on the network-based on random walks. SNEA integrates the advantages of the two algorithms to dynamically grasp the evolutionary rules of the network. Then this paper proposes a triad link prediction algorithm (TLPA) to quantitatively evaluate the results of the evolution analysis of the SNEA method. The TLPA algorithm reduces the blindness of message forwarding and unnecessary waste of resources by predicting the probability of a connection between nodes in the network. Experimental results show that compared with Epidemic, SECM, CRDNT, ICMT algorithms, our method has a prominent advantage in improving message delivery rate and reducing resource consumption.