Betweenness centrality-based community adaptive network representation for link prediction

Sequential stacking link prediction algorithms for temporal networks

Link prediction algorithms are indispensable tools in many scientific applications by speeding up network data collection and imputing missing connections. However, in many systems, links change over time and it remains unclear how to optimally exploit such temporal information for link predictions in such networks. Here, we show that many temporal topological features, in addition to having high computational cost, are less accurate in temporal link prediction than sequentially stacked static network features. This sequential stacking link prediction method uses 41 static network features that avoid detailed feature engineering choices and is capable of learning a highly accurate predictive distribution of future connections from historical data. We demonstrate that this algorithm works well for both partially observed and completely unobserved target layers, and on two temporal stochastic block models achieves near-oracle-level performance when combined with other single predictor methods as an ensemble learning method. Finally, we empirically illustrate that stacking multiple predictive methods together further improves performance on 19 real-world temporal networks from different domains.

The Symptom Network Structure of Teachers' Burnout and Its Connection to Psychological Capital

Background

Teacher burnout is characterized by emotional and physical exhaustion resulting from excessive work-related stress. Previous research based on traditional latent variable theory has revealed a significant negative relationship between TB and psychological capital (PsyCap). This study explored the complex symptoms of TB and the contact points of PsyCap in reducing TB using psychometric network analysis.

Methods

A total of 3991 teachers completed the burnout subscale of the Professional Quality of Life and Psychological Capital Scale.

Results

The results showed that: (a) In the TB network, the core symptoms displayed by teachers due to burnout are difficulty feeling "I am a very caring person", "I am happy", and "I am the person I always wanted to be"; (b) The TB-PsyCap network was closely connected, and the symptoms affected each other. PsyCap affected the TB network through "I feel optimistic and happy almost every day" and "I often feel that there is a future as a teacher"; (c) PsyCap's bridge symptoms had a negative impact on TB, and PsyCap may reduce TB primarily through optimistic, hopeful dimensions.

Conclusion and Implications

Psychometric network analysis helps us understand the complex symptoms of TB and the contact points of PsyCap in reducing TB. This study offers valuable insights into the prevention of, and intervention in, burnout within the teaching community.

The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm

One of the main problems encountered by social networks is the cold start problem. The term “cold start problem” refers to the difficulty in predicting new users’ friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using “betweenness centrality,” and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the “products rapidly attracting users” in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles.