Spreading dynamics analysis is an important and interesting topic since it has many applications such as rumor or disease controlling, viral marketing and information recommending. Many state-of-the-art researches focus on predicting infection scale or threshold. Few researchers pay attention to the predicting of infection nodes from a snapshot. With developing of precision marketing, recommending and, controlling, how to predict infection nodes precisely from snapshot becomes a key issue in spreading dynamics analysis. In this paper, a probability based prediction model is presented so as to estimate the infection nodes from a snapshot of spreading. Experimental results on synthetic and real networks demonstrate that the model proposed could predict the infection nodes precisely in the sense of probability.

Reward is an effective mechanism that promotes cooperation. However, an individual usually reward her opponents in certain cases. Inspired by this, a conditional neutral reward mechanism has been introduced. In detail, an individual will reward his or her neighbors with the same strategy when the payoff of the focal one is higher than that of his or her neighbors. And simulations are conducted to investigate the impact of our mechanism on the evolution of cooperation. Interestingly, cooperation can survive and dominate the system. Nominal antisocial reward that defectors reward each other is rarely because of the greed of defectors. By contrast, cooperators inside the cooperative clusters share the payoff with cooperators on the boundary so that the latter can form shields to protect cooperators.

Heterogeneous nature of real networks implies that different edges play different roles in network structure and functions, and thus to identify significant edges is of high value in both theoretical studies and practical applications. We propose the so-called second-order neighborhood (SN) index to quantify an edge's significance in a network. We compare SN index with many other benchmark methods based on 15 real networks via edge percolation. Results show that the proposed SN index outperforms other well-known methods.