In recent years, Graph Contrastive Learning (GCL) has emerged as a key research direction for learning representations of unlabeled graph data, focusing on the self-supervised learning of efficient representations for both graphs and nodes. However, existing approaches typically assume feature homogeneity across different augmented views, overlooking the potential impact of inter-view feature differences, particularly weak features, on model performance. To address the problem of weak features and the feature differences between different enhanced views, this paper proposes the Feature Discrepancy-Aware Graph Contrastive Learning (FDAGCL) framework. Firstly, FDAGCL dynamically adjusts the importance of features through the feature importance decoupling mechanism, thereby effectively distinguishing strong features from weak view. Secondly, FDAGCL designs a multi-view map contrastive learning strategy to enhance the expression of strong features while simultaneously improving the learning of weak features through strong-strong view and strong-weak view map contrastive learning, thereby achieving information complementarity. To validate the effectiveness of our method, we conducted extensive empirical experiments on five datasets. The results demonstrate that FDAGCL exhibits significant superiority over the baseline methods.

Abstract Currently, most researchers only utilize the network information or node characteristics to calculate the connection probability between unconnected node pairs. Therefore, we attempt to project the problem of connection probability between unconnected pairs into the physical space calculating it. Firstly, the definition of gravitation is introduced in this paper, and the concept of gravitation is used to measure the strength of the relationship between nodes in complex networks. It is generally known that the gravitational value is related to the mass of objects and the distance between objects. In complex networks, the interrelationship between nodes is related to the characteristics, degree, betweenness, and importance of the nodes themselves, as well as the distance between nodes, which is very similar to the gravitational relationship between objects. Therefore, the importance of nodes is used to measure the mass property in the universal gravitational equation and the similarity between nodes is used to measure the distance property in the universal gravitational equation, and then a complex network model is constructed from physical space. Secondly, the direct and indirect gravitational values between nodes are considered, and a novel link prediction framework based on the gravitational field, abbreviated as LPFGF, is proposed, as well as the node similarity framework equation. Then, the framework is extended to various link prediction algorithms such as Common Neighbors (CN), Adamic-Adar (AA), Preferential Attachment (PA), and Local Random Walk (LRW), resulting in the proposed link prediction algorithms LPFGF-CN, LPFGF-AA, LPFGF-PA, LPFGF-LRW, and so on. Finally, four real datasets are used to compare prediction performance, and the results demonstrate that the proposed algorithmic framework can successfully improve the prediction performance of other link prediction algorithms, with a maximum improvement of 15%.

Currently, most existing link prediction algorithms simply study the interrelationships between node pairs without considering the interaction force and the higher order relationships between node pairs. In order to find a solution to this problem, the concept of the gravitational field is introduced in this article, and then, a novel algorithmic framework is proposed from the perspective of physics. The framework is applied to the classic link prediction algorithms to effectively enhance their prediction performance. First, the node contraction method is applied to measure the node importance, and a similarity-based link prediction algorithm is used to calculate the similarity values between node pairs. Second, the importance of nodes is introduced into the gravitational field model as the mass attribute, and the similarity values between node pairs are used as a distance metric between node pairs. Thereby, a gravitational field model of the complex network from the perspective of physics is established. Finally, the edges of the undirected complex network are assigned the weights, and a weighted local random walking-based link prediction algorithm is proposed. The link prediction method is adopted to evaluate the reasonableness and practical value of the gravitational field model. Experimental results show that most link prediction algorithms using the proposed algorithmic framework have got improvement with a minimum improvement of 2% and a maximum improvement of 33%; thus, the effectiveness and feasibility of the algorithm are verified.

We present the design and results of an experiment investigating the occurrence of self-illusion and its contribution to realistic behavior consistent with a virtual role in virtual environments. Self-illusion is a generalized illusion about one's self in cognition, eliciting a sense of being associated with a role in a virtual world, despite sure knowledge that this role is not the actual self in the real world. We validate and measure self-illusion through an experiment where each participant occupies a non-human perspective and plays a non-human role using this role's behavior patterns. 77 participants were enrolled for the user study according to the priori power analysis. In the mixed-design experiment with different levels of manipulations, we asked the participants to play a cat (a non-human role) within an immersive VE and captured their different kinds of responses, finding that the participants with higher self-illusion can connect themselves to the virtual role more easily. Based on statistical analysis of questionnaires and behavior data, there is some evidence that self-illusion can be considered a novel psychological component of presence because it is dissociated from sense of embodiment (SoE), plausibility illusion (Psi), and place illusion (PI). Moreover, self-illusion has the potential to be an effective evaluation metric for user experience in a virtual reality system for certain applications.

With the widespread use of the internet, exploring how it will influence the labor market is of great significance. Based on the 2010-2018 China Family Panel Studies dataset, this paper investigates the effect of the internet on sustainable employability among Chinese aged 16-60. The empirical results of the panel double-hurdle model show that the internet can significantly enhance an individual's competitiveness in the labor market. Moreover, the heterogeneity tests show that the middle aged and older adults, freelancers, and those living in disadvantaged regions can benefit more on employability brought about by the internet. The authors define this phenomenon as the information welfare of the internet, which has narrowed the digital gap caused by the uneven development of technology among different social groups. In addition, the positive coefficient associated with internet use is driven by higher skill requirements in specific workplaces. The authors further explored the role workplace computerization has had in this process.