Daily-deal platforms closely cooperate with local retailers when issuing daily-deal coupons to profit from selling coupons online and redeeming them offline. However, most research on daily-deal business has only focused on online sales or the offline redemption process. We investigate the coherent two-phased process from selling coupons online to redeeming them offline, grounded in the lens of social judgment theory, to capture the full picture of the daily-deal business. By tracking the sales and redemption of 11,290 deals over a 13-month period on an online daily-deal platform and conducting various data analyses, we find that reputation and price curvilinearly affect the sold online of daily-deal coupons, which consequently positively affects coupon redemption offline. More specifically, the U test empirically indicates that the extreme point of the inverted U-shaped effect of reputation score is 86.0035 within the range [49.7353, 92.7551]. And the extreme point to price demonstrates a U-shaped effect is 399.6082 within the range [4.7060, 829.3651]. We further classify retailers’ daily deals into consumption on a group or individual level. Empirical data demonstrate that the inverted U-shaped effects of reputation and the U-shaped effects of price are weakened by group consumption. Furthermore, we investigate the moderating role of agglomeration on the relationship between daily-deal coupons sold online and redemption offline of daily-deal coupons. We also discussed the theoretical and practical implications.

The topological and dynamical features of complex networks hold abundant information. How to fully utilize this information for more accurate network structure mining is a significant issue. In this paper, we propose a novel method that simultaneously takes into account both network topological and dynamical features via graph convolutional networks (TD-GCN). Specifically, we obtain the topological features of the network by using the second-order adjacency matrix of the complex network, which captures indirect connections between nodes, for a more detailed representation of network structure, and use the SIS model to generate node state data in the complex network as the dynamical features of the network. The network topological and dynamical features are fused through the graph convolutional neural network. To verify the effectiveness and applicability of our method, we conduct extensive experiments on both simulated networks and real-world networks with various network scales. We comprehensively compare the proposed method with other existing methods in the domains of network link prediction and network node ranking learning. The experimental results show that our method can better capture the characteristic information in complex networks and has better performance compared with other methods.

The budgeted influence maximization (BIM) problem aims to identify a set of seed nodes that adhere to predefined budget constraints within a specified network structure and cost model. However, it is difficult for the existing algorithms to achieve a balance between timeliness and effectiveness. To address this challenge, our study initially proposes a refined cost model through empirical scrutiny of Weibo's quote data. Subsequently, we introduce a proxy-based algorithm, i.e., the budget-aware local influence iterative (BLII) algorithm tailored for the BIM problem, aimed at expediently identifying seed nodes. The algorithm approximates the global influence by leveraging the user's one-hop influence and circumvents influence overlap among seed nodes via iterative influence updates. Comparative experiments involving eight algorithms across four real networks demonstrate the effectiveness, efficiency, and robustness of the BLII algorithm. In terms of influence spread, the proposed algorithm outperforms other proxy-based algorithms by 20%-255 % and reaches the state-of-the-art simulation-based approach by 96 %. In addition, the running time of the BLII algorithm is reasonable. Generally, the proposed cost model and BLII algorithm provide novel insights and potent tools for studying BIM problems.

The digital transformation of the innovation ecosystem is not only an inevitable direction of innovation activities in the era of digital economy but also a highly complex and uncertain process. The way to facilitate transformation with policies has become a topic of common concern of academia and policymakers. This paper builds a multiagent model and studies the impacts of supply-side policies, demand-side policies, and environmental policies on enterprises’ transformation willingness, digital level, and income level as well as the proportion of enterprises that carry out transformation in the whole innovation ecosystem and innovation network structure by numerical experiments. According to research findings, supply-side policies play the biggest role in the facilitation of transformation, demand-side policies are second important to them, and environmental policies have comparatively weak impacts.

Regional integrated energy system (RIES) provides a platform for coupling utilization of multi-energy and makes various energy demand from client possible. The suitable RIES composition scheme will upgrade energy structure and improve integrated energy utilization efficiency. Based on a RIES construction project in Jiangsu province, this paper proposes a new multi criteria decision-making (MCDM) method for the selection of RIES schemes. Because that subjective evaluation on RIES schemes benefit under criteria has uncertainty and hesitancy, intuitionistic trapezoidal fuzzy number (ITFN) which has the better capability to model ill-known quantities is presented. In consideration of risk attitude and interdependency of criteria, a new decision model with risk coefficients, Mahalanobis-Taguchi system and Choquet integral is proposed. Firstly, the decision matrices given by experts are normalized, and then are transformed to minimum expectation matrices according to different risk coefficients. Secondly, the weights of criteria from different experts are calculated by Mahalanobis-Taguchi system. Mobius transformation coefficients based on interaction degree are to calculate 2-order additive fuzzy measures, and then the comprehensive weights of criteria are obtained by fuzzy measures and Choquet integral. Thirdly, based on group decision consensus requirement, the weights of experts are obtained by the maximum entropy and grey correlation. Fourthly, the minimum expectation matrices are aggregated by the intuitionistic trapezoidal fuzzy Bonferroni mean operator. Thus, the ranking result according to the comparison rules using the minimum expectation and the maximum expectation is obtained. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.

Information diffusion is an important branch of online social network analysis. In this paper, we construct a new metric, the proportion of leaf nodes in a diffusion tree (L-metric), to quantify information diffusion patterns, and we study the impact of the network category and information content on these patterns. Simulation-based experimental studies of real-world social networks show that information diffusion exhibits different patterns in different networks, and niche information does not typically propagate easily in any type of network. These conclusions provide a new perspective for further research on management decisions with regard to online social networks.

According to the requirement of energy sustainable development strategy in Jilin province, this paper evaluates the performance of wind power coupling compressed air energy storage projects for a wind farm in Jilin from the perspective of sustainability. Firstly, a sustainable criteria system is established from economic, social and ecological dimensions, and 10 secondary criteria are given. Secondly, to fully express the hesitancy and fuzziness of experts, linguistic fuzzy hesitant sets (LFHSs) are used to describe the subjective evaluation information. And then, the cloud model is developed to deal with the randomness and fuzziness of LFHS. Thirdly, a multi criteria decision-making model based on improved Choquet fuzzy integral and deviation degree is proposed for a multi criteria evaluation problem with initial data in the form of crisp values, interval numbers and linguistic fuzzy hesitant sets. The deviation degree can deal with multi-source heterogeneous data and Choquet fuzzy integral improved with Mobius transformation can eliminate the overlap information between criteria. Finally, the case study in Jilin is given to illustrate the effective of the proposed evaluation criteria and technique. From the results, the project 4: wind power coupling compressed air energy storage system with meshing switch is the most suitable alternative for the wand farm, followed by the project of wind power coupling compressed air energy storage system with variable configuration. Moreover, the sensitivity analysis reveals that the project 4 always occupies its top ranking when its weight is floating within a certain range. At last, some policy implications to promote wind power industry are given for the government.

Information propagation modeling and influence maximization are two important research problems in viral marketing. When marketing information is given, how can the seed nodes be efficiently identified to maximize the spread of the information through the network? To answer this question, we consider multiple factors in information propagation, such as information content, social influence and user authority, and propose a multifactor-based information propagation model (MFIP). Then, we utilize the first-order influence of the nodes to approximate their influence and propose an efficient heuristic algorithm named weighted degree decrease (WDD) to select the seed nodes under the MFIP model. Experimental evaluations with four real-world social network datasets demonstrate the effectiveness and efficiency of our algorithm.

针对现有社交网络生成模型在解释网络中观结构和微观特性上的不足,提出了考虑社会选择作用的BA模型(SSBA).该模型基于社交网络用户的特征分布和同质性形成的内在机理,在建模过程中考虑了社会影响和社会选择的共同作用.在仿真数据与真实数据集上的实验表明,SSBA模型能很好地刻画不同类型社交网络在宏观特性和中观结构上的特点.发现不同作用机制下网络表现出相异的统计特性,社会选择作用较强时,网络度分布逐渐偏离幂律分布,且网络逐渐向同配网络转变.

Individuals in the group have significant difference in initial opinion or attitude towards some problems due to the distinction of their background knowledge,personality traits and own interests, etc.,which will directly affect their communication intentions and views update speed.In this paper, a new bounded confidence model with the theory of"first impressions are most lasting"is established under the Weibuch-Deffuant model to deal with the effects of initial views on opinion evolution.Some computer simulations are given to demonstrate that when the impact factor of initial view s γ is be-tween 0 and 1,the convergence rate of opinion dynamics significantly slows down and the proportion of population on both sides increases.Furthermore,the amount of opinion clusters and the ratio of maximum cluster change substantially,which shows that the group points are scattered and unfavor-able to reach consensus.The results can reflect and explain the phenomenon of the"first impressions are most lasting"to some extent.

Previous studies have used several models to investigate the mechanisms for growing and evolving real social networks. These models have been widely used to simulate large networks in many applications. In this paper, based on the evolutionary mechanisms of homophily and popularity, we propose a new generation model for growing and evolving social networks, namely, the Homophily-Popularity model. In this new model, new links are added, and old links are deleted based on the link probabilities between every node pair. The results of our simulation-based experimental studies provide evidence that the proposed model is capable of modelling a variety of real social networks.

The influence maximization problem is widely studied, but previous studies have assumed that the cost to activate each seed node was identical. In this paper, we consider different activation costs and investigate a new problem: the budget-aware influence maximization problem (BIM). This problem is NP-hard, which motivates our interest in its approximation. We develop two greedy algorithms for BIM, namely, BG and GMUI, and show that GMUI obtains a solution that is provably . Then, we consider the average profit to activate a common user and introduce GMUN, an improved GMUI algorithm. Finally, we evaluate our algorithms with experiments using two large real social networks. The results show that GMUI performs best in terms of influence, whereas GMUN creates leverage between the budget and net income, indicating that the marginal net income/costs ratio for each seed node selected by GMUN can satisfy enterprises.

In order to study the effect of the opinion leader on opinion evolution ,the rule network structure with the center is constructed .Several factors that affect the opinion evolution are analyzed , including the network structure ,local nonlinear dynamic behavior and different ways of interaction be-tween individuals .Then the synchronous conditions are derived according to the matrix theory and the Lyapunov stability theory .The theoretical and simulation analysis results show that when the individ-ual local nonlinear dynamic system is stable ,the opinion leader has effect on the convergence rate of synchronization ;but when it is weak or unstable in some area ,he has stronger adjustment ability to synchronization than general population .

Many enterprises try to promote their products in online social network since information propagation in this network have several advantages such as fast transmission speed, low marketing costs, and large influence area. However, it is a challenging task for enterprises to select suitable seed nodes to publish marketing information so that marketing information can influence or cover most users under a given cost and realize performance maximization. By means of literature search and review, this paper systematically summarizes information propagation models in social marketing, introduces algorithms for social marketing performance maximization problem with respect to network topology, user historical data, compete and non-compete condition. Finally, this paper concludes an exploration of future directions of this research filed.