# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 1 Introduction  
Complex networks simplify the complex systems that are found in the real world. The research on complex networks can help people to deeply understand them, such as their internal dynamic evolution and for behavior prediction [1; 2; 3]. Some nodes in complex networks play key roles in the information dissemination of entire networks; these are called influential nodes. Identifying influential nodes in complex networks is an important direction in the field of complex network research [4], and has practical value for information dissemination in real-world networks [5], spread of infectious diseases [6], product promotion [7; 8], etc. This task can effectively reduce economic cost and avoid economic loss to a certain extent [9]. For example, in traffic networks [10], the identification of the influential nodes of roads can be used as an important reference for road resource allocation and flow diversion. Identifying the influential nodes in biological networks [11] can provide auxiliary means for disease treatment and understanding of biological information. In recent decades, from structural centrality and iterative refinement of centrality to dynamic operation based on nodes, many algorithms for identifying influential nodes have been proposed in the field of complex networks. For instance, the degree centrality (DC) [12] algorithm judges the importance of the nodes by counting the number of their directly connected neighbor nodes. [PERSON] et al. [13] considered the semi-local information of nodes and proposed semi-local centrality. Betweenness centrality (BC) [14] describes the ability of nodes to control the information flow along the shortest path in the network. [PERSON] [15] considered that the shortest paths were more important than the non-shortest paths in the process of information propagation, and proposed the random walk betweenness centrality. The K-shell [16] algorithm strips the nodes in the outer layer of the network, and considers the nodes at the core of the network has having strong influence. As a node deletion method, it can avoid problems arising from the unsuitable selection of attributes and indicators during the analysis of social networks by reverse thinking [17]. The feature vector centrality [12] uses the influence value of adjacent nodes as their influence indicator. The PageRank (PR) [18] algorithm is a variant of feature vector centrality. As a web page sorting algorithm applied in the Google search engine, PageRank considers each web page as a node and hyperlinks between pages as edges between nodes. The influence indicator of the node is defined as the PR value. Each node evenly distributes its PR value to neighboring nodes. The LeaderRank algorithm proposed by [PERSON] et al. [19] identifies influential nodes using the idea of the random walk, which performs well in directed networks. [PERSON] proposed an algorithm named HITS that assigns each node an authority value and a hub value, which affect each other as the indicator used to evaluate the influence of nodes [20]. In recent years, some algorithms based on the H-index have been proposed [21; 22]. The H-index is an indicator based on citation frequency in the paper citation network [23]. These are essentially node-degree-based algorithms. The above algorithms describe the influence of nodes from different perspectives and each method has its own advantages, but they also have certain limitations. For example, DC is the most intuitive algorithm but does not consider the location of nodes or the influence of surrounding nodes [24]; K-shell often assigns the same influence value to a large number of nodes in some special networks, such as tree networks. PageRank distributes its own PR values evenly to adjacent nodes, which are deviated from actual cognition. BC and the algorithms based on node deletion or contraction are time-consuming, and some algorithms are not effective in disconnected networks.