Influential nodes are rare in social networks, but their influence can quickly spread to most nodes in the network. Identifying influential nodes allows us to better control epidemic outbreaks, accelerate information propagation, conduct successful e-commerce advertisements, and so on. Classic methods for ranking influential nodes have limitations because they ignore the impact of the topology of neighbor nodes on a node. To solve this problem, we propose a novel measure based on local centrality with a coefficient. The proposed algorithm considers both the topological connections among neighbors and the number of neighbor nodes. First, we compute the number of neighbor nodes to identify nodes in cluster centers and those that exhibit the “bridge” property. Then, we construct a decreasing function for the local clustering coefficient of nodes, called the coefficient of local centrality, which ranks nodes that have the same number of four-layer neighbors. We perform experiments to measure node influence on both real and computer-generated networks using six measures: Degree Centrality, Betweenness Centrality, Closeness Centrality, K-Shell, Semi-local Centrality and our measure. The results show that the rankings obtained by the proposed measure are most similar to those of the benchmark Susceptible-Infected-Recovered model, thus verifying that our measure more accurately reflects the influence of nodes than do the other measures. Further, among the six tested measures, our method distinguishes node influence most effectively.

In social networks, influential spreaders are those nodes that can spread information to a large number of nodes. Identifying influential spreaders is a major challenge for applications such as information diffusion acceleration, epidemic outbreak prevention, and effective e-commerce advertisement conduction. Existing methods consider the influence of a node’s neighbors on its spreading ability but rarely account for the topology of the neighboring nodes. Therefore, we propose a novel measure based on normalized local structure attributes, called normalized local centrality, that considers the topology of the local network around a node as well as the influence feedback of the node’s nearest neighbor nodes. First, we compute the influence of a node’s neighbors and the local clustering coefficient of them to identify nodes in cluster centers and those function as “bridge”. Then, a normalization function is designed to normalize the results to avoid adding new variable parameters. We perform experiments to identify influential spreaders in both real and computer-generated networks and compare the results on the basis of seven measures: degree, betweenness, closeness, k-shell, semi-local centrality, local structure centrality and our proposed measure. In the susceptible–infected–recovered model, the node influence rankings obtained by our measure are most consistent with those of the benchmark, thus confirming that our method measures node influence more accurately than the other methods. Furthermore, the top-100 nodes ranked by our method lead to faster and wider spread than those ranked by the other six tested measures.

Click-through rate (CTR) prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment. In the CTR prediction, the interaction between features is a key factor affecting the prediction rate. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition and combines the power of field-aware factorization machines and deep learning to portray the nonlinear associated relationship of data to solve the sparse feature learning problem. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in Internet advertising.

Identification of the most influential spreaders that maximize information propagation in social networks is a classic optimization problem, called the influence maximization (IM) problem. A reasonable diffusion model that can accurately simulate information propagation in social networks is the key step to efficiently solving the IM problem. Synergism of neighbor nodes plays an important role in information propagation dynamics. Some known diffusion models have considered the reinforcement mechanism in defining the activation threshold. Most of these models focus on the synergetic effects of nodes on their common neighbors, but the accumulation of synergism has been neglected in previous studies. Inspired by these facts, we first discuss the catalytic role of synergism in the spreading dynamics of social networks and then propose a novel diffusion model called the synergism-based three-step cascade model (TSSCM) based on the above analysis and the three-degree influence theory. Finally, we devise an algorithm for solving the IM problem based on the TSSCM. Experiments on five real large-scale social networks demonstrate the efficacy of our method, which achieves competitive results in terms of influence spreading compared to the four other algorithms tested.

Currently, online social networks are experiencing explosive growth, and play important roles in all aspects of lives, such as daily communication, online study and online dating. People's everyday life can't stand away from online social networks. With the advances of online social networks and popularity of intelligent mobile devices, more and more service providers are marketing their own online social network services, which providing competent services and ample functionalities. Nonetheless, almost all the mainstream social networks are based on the centralized servers or distributed server clusters. The whole system functions on the basis of the servers, so users depend excessively on servers. This kind of system architecture has potential single-point or multi-point failures though it can offer superior services to users. Users can't log into the system to obtain social services when the servers run into faults, such as equipment failures, link failures or network attacks. More seriously, ifcentralized databases meet with troubles, physical damages or mistake operations, massive user data perhaps could not be used or even lost. This paper defines the problem as Local Service Fault Partition (LSFP) and proposes a novel social network model called HPOSN which applies thoughts of P2P to solve LSFP and optimizes the centralized social network services. The prototype of HPOSN optimizes the communication overhead to control the flooding problem of P2P according to social relationship. Several simulation experiments with two parameters have been conducted, including the Recovery Time and the Recovery Success Rate. Results indicate that HPOSN can solve LSFP problem in centralized social networks pragmatically. Users in the LSFP area can recovery the social services locally through selforganizing and self-managing when they lose the services from centralized server or distributed server clusters. So HPOSN can improve the stability of social network services and user experiences.