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As information explosively grows, it requires to find out the highly efficient data from the global database. In this paper, based on two real systems, i.e., the MovieLens and Netflix, we investigate the temporal information effect on personalized recommendation for three collaborative filtering algorithms, by considering both the volume and time of the training data. It is observed that, for an increasing training data of a certain time range, the recommendation accuracy using the time-dependent training data outperforms that using the random training data of the same size. Moreover, the increase of data volume would not always improve the recommendation accuracy for the time-dependent training data case, whereas the recent temporal training data are important to elevate the recommendation accuracy. Further study on a hybrid algorithm of heat conduction and mass diffusion shows that, using the recent temporal information as training data, the algorithm reaches the optimal recommendation accuracy, when it returns to the original mass diffusion algorithm, which suggests no necessity of introducing an extra parameter. And the simple mass diffusion algorithm is found to be more advantageous than the previous three algorithms.

The hierarchical clustering algorithm has been proved useful in portfolio investment, which is one of the hottest issues in finance. In our new portfolio strategy, central, peripheral and dispersed portfolios constructed from clusters detected using unweighted and weighted modularity are compared according to their past performances, and the optimal portfolio is used in the investment period only if the market index return predicted by the LR, WMA or BP models is positive to avoid losses when the market drops. Our strategy is tested using the daily data of Chinese A-share market from January 4, 2008 and December 31, 2016, and the average investment return during different moving investment periods and 200 repeated runs is calculated. We find that although incorporating dispersed portfolio into our strategy has no significant effect in raising the investment return, it shows a similar performance as the peripheral portfolio, and the strategy constructed using unweighted modularity generally outperforms its counterpart by using weighted modularity. In addition, the market trend prediction can refine the investment return of our strategy. In brief, the strategy constructed using the BP model and unweighted modularity has the best investment return, which also outperforms the Markowitz portfolio.

How evolution favors cooperation is a fundamental issue in social and economic systems. In the business world, actively selecting a suitable project is usually helpful for a businessman to be in an advantageous position. By incorporating project selection mechanism into the threshold public goods game, we have investigated the coupling effect of mutation and imitation in updating one’s preferred project scale on the evolution of cooperation. Compared with the situation where there is no project selection mechanism, the existence of project selection may suppress or promote cooperation depending upon different updating rules. There exists a critical ratio of the imitators in the population, below which cooperation is suppressed while above which cooperation is promoted. With the coevolving of individual strategies and preferred project scales, a higher level of cooperation corresponds to a larger average value of the preferred project scales. A theoretical analysis indicates that, as most of the individuals are mutants, the coevolving process is governed by the mutation process, which leads to a smaller average value of the preferred project scales and a lower level of cooperation. As most of the individuals are imitators, the coevolving process is governed by the coupling of the mutation and imitation processes, which leads to a larger average value of the preferred project scales and a higher level of cooperation. As all the individuals are imitators, the coevolving process is governed by the imitation process, which leads to an intermediate average value of the preferred project scales and an intermediate level of cooperation.

Facing a heavy task, any single person can only make a limited contribution and team cooperation is needed. As one enjoys the benefit of the public goods, the potential benefits of the project are not always maximized and may be partly wasted. By incorporating individual ability and project benefit into the original public goods game, we study the coupling effect of the four parameters, the upper limit of individual contribution, the upper limit of individual benefit, the needed project cost and the upper limit of project benefit on the evolution of cooperation. Coevolving with the individual-level group size preferences, an increase in the upper limit of individual benefit promotes cooperation while an increase in the upper limit of individual contribution inhibits cooperation. The coupling of the upper limit of individual contribution and the needed project cost determines the critical point of the upper limit of project benefit, where the equilibrium frequency of cooperators reaches its highest level. Above the critical point, an increase in the upper limit of project benefit inhibits cooperation. The evolution of cooperation is closely related to the preferred group-size distribution. A functional relation between the frequency of cooperators and the dominant group size is found.

The high-order redundant correlation effect is investigated for a hybrid algorithm of heat conduction and mass diffusion (HHM), through both heat conduction biased (HCB) and mass diffusion biased (MDB) correlation redundancy elimination processes. The HCB and MDB algorithms do not introduce any additional tunable parameters, but keep the simple character of the original HHM. Based on two empirical datasets, the Netflix and MovieLens , the HCB and MDB are found to show better recommendation accuracy for both the overall objects and the cold objects than the HHM algorithm. Our work suggests that properly eliminating the high-order redundant correlations can provide a simple and effective approach to accurate recommendation.

In nature and society, individuals are often tempted to exploit free-riding opportunities. Previous studies have shown that, without additional measures like spatially restricted interactions and costly punishment, defection should be more prevalent than cooperation. By incorporating group sensitivity and size adjustable interacting groups into the N -person snowdrift game, we have studied the coevolution of the preferred group sizes and the frequencies of cooperators. It is found that the widespread of cooperation is closely related to the degree of an individual’s group sensitivity. In comparison with a weak or a strong sensitivity, a moderate group sensitivity can greatly promote cooperation. With the rise of the group sensitivity, the step-like changes of the frequencies of cooperators and the average preferred group sizes are found. A theoretical analysis reveals that an individual’s group sensitivity may lead to a decrease in the preferred group size and accordingly an increase in cooperation. A functional relation between the frequencies of cooperators and the average preferred group sizes is found.

Financial systems are complex systems which have been widely studied in recent years. We here propose a model to study stock correlations in financial markets, in which an agent's expected return for one stock is influenced by the historical return of the other stock. Each agent makes a decision based on his expected return with reference to information dissemination and the historical return of the stock. We find that the returns of the stocks are positively (negatively) correlated when agents' expected returns for one stock are positively (negatively) correlated with the historical return of the other. We provide both numerical and analytical studies and give explanations to stock correlations for cases with agents having either homogeneous or heterogeneous expected returns. The result still holds when other factors such as holding decisions and external events are included which broadens the practicability of the model.

In a static environment, optional participation and a local agglomeration of cooperators are found to be beneficial for the occurrence and maintenance of cooperation. In the optional public goods game, the rock–scissors–paper cycles of different strategies yield oscillatory cooperation but not stable cooperation. In this paper, by incorporating population density and individual mobility into the spatial optional public goods game, we study the coevolutionary dynamics of strategy updating and benefit-seeking migration. With low population density and slow movement, an optimal level of cooperation is easy to be reached. An increase in population density and speed-up of free-floating of competitive agents will suppress cooperation. A log–log relation between the levels of cooperation and the free-floating probability is found. Theoretical analysis indicates that the decrease of cooperator frequency in the present model should result from the increased interactions between different agents, which may originate from the increased cluster size or the speed-up of random-movement.

Cooperation in the evolutionary snowdrift game with a self-questioning updating mechanism is studied on annealed and quenched small-world networks with directed couplings. Around the payoff parameter value $r=0.5$, we find a size-invariant symmetrical cooperation effect. While generally suppressing cooperation for $r>0.5$ payoffs, rewired networks facilitated cooperative behavior for $r, 7 pages, 6 figures, 1 table

Incorporating dynamic contact networks and delayed awareness into a contagion model with memory, we study the spreading patterns of infectious diseases in connected populations. It is found that the spread of an infectious disease is not only related to the past exposures of an individual to the infected but also to the time scales of risk perception reflected in the social network adaptation. The epidemic threshold $p_{c}$ is found to decrease with the rise of the time scale parameter s and the memory length T, they satisfy the equation $p_{c} =\frac{1}{T}+ \frac{\omega T}{a^s(1-e^{-\omega T^2/a^s})}$. Both the lifetime of the epidemic and the topological property of the evolved network are considered. The standard deviation $\sigma_{d}$ of the degree distribution increases with the rise of the absorbing time $t_{c}$, a power-law relation $\sigma_{d}=mt_{c}^\gamma$ is found.

The effects of networking on the extent of cooperation emerging in a competitive setting are studied. The evolutionary snowdrift game, which represents a realistic alternative to the well-known Prisoner's Dilemma, is studied in the Watts-Strogatz network that spans the regular, small-world, and random networks through random re-wiring. Over a wide range of payoffs, a re-wired network is found to suppress cooperation when compared with a well-mixed or fully connected system. Two extinction payoffs, that characterize the emergence of a homogeneous steady state, are identified. It is found that, unlike in the Prisoner's Dilemma, the standard deviation of the degree distribution is the dominant network property that governs the extinction payoffs., Changed content

The probability distribution of the time intervals between two consecutive accidents is investigated, based on the road accidental records of the Great Britain. A universal description is obtained for different roads, by rescaling the probability distribution and time intervals. The scaling curve is found to deviate from the Gaussian distribution, but it is well fitted by a stretched exponential function. Long-range time correlation is revealed for the interevent series. Moreover, gender similarity is found for the small accidental intervals, while for the large intervals, the female drivers are observed to present a higher probability than the male drivers.

Recommendation bias towards objects has been found to have an impact on personalized recommendation, since objects present heterogeneous characteristics in some network-based recommender systems. In this article, based on a biased heat conduction recommendation algorithm (BHC) which considers the heterogeneity of the target objects, we propose a heterogeneous heat conduction algorithm (HHC), by further taking the heterogeneity of the source objects into account. Tested on three real datasets, the Netflix, RYM and MovieLens, the HHC algorithm is found to present better recommendation in both the accuracy and diversity than two benchmark algorithms, i.e., the original BHC and a hybrid algorithm of heat conduction and mass diffusion (HHM), while not requiring any other accessorial information or parameter. Moreover, the HHC algorithm also elevates the recommendation accuracy on cold objects, referring to the so-called cold-start problem. Eigenvalue analyses show that, the HHC algorithm effectively alleviates the recommendation bias towards objects with different level of popularity, which is beneficial to solving the accuracy-diversity dilemma.