Université de Fribourg

Discoverers in scientific citation data

Shi, Gui-Yuan ; Kong, Yi-Xiu ; Yuan, Guang-Hui ; Wu, Rui-Jie ; Zeng, An ; Medo, Matúš

In: Journal of Informetrics, 2019, vol. 13, no. 2, p. 717–725

Identifying the future influential papers among the newly published ones is an important yet challenging issue in bibliometrics. As newly published papers have no or limited citation history, linear extrapolation of their citation counts—which is motivated by the well-known preferential attachment mechanism—is not applicable. We translate the recently introduced notion of discoverers to...

Université de Fribourg

Structure-oriented prediction in complex networks

Ren, Zhuo-Ming ; Zeng, An ; Zhang, Yi-Cheng

In: Physics Reports, 2018, vol. 750, p. 1–51

Complex systems are extremely hard to predict due to its highly nonlinear interactions and rich emergent properties. Thanks to the rapid development of network science, our understanding of the structure of real complex systems and the dynamics on them has been remarkably deepened, which meanwhile largely stimulates the growth of effective prediction approaches on these systems. In this...

Université de Fribourg

Identification and impact of discoverers in online social systems

Medo, Matúš ; Mariani, Manuel S. ; Zeng, An ; Zhang, Yi-Cheng

In: Scientific Reports, 2016, vol. 6, p. 34218

Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by...

Université de Fribourg

The effect of heterogeneous dynamics of online users on information filtering

Chen, Bo-Lun ; Zeng, An ; Chen, , Ling

In: Physics Letters A, 2015, vol. 379, no. 43–44, p. 2839–2844

The rapid expansion of the Internet requires effective information filtering techniques to extract the most essential and relevant information for online users. Many recommendation algorithms have been proposed to predict the future items that a given user might be interested in. However, there is an important issue that has always been ignored so far in related works, namely the heterogeneous...

Université de Fribourg

Predicting missing links via correlation between nodes

Liao, Hao ; Zeng, An ; Zhang, Yi-Cheng

In: Physica A: Statistical Mechanics and its Applications, 2015, vol. 436, p. 216–223

As a fundamental problem in many different fields, link prediction aims to estimate the likelihood of an existing link between two nodes based on the observed information. Since this problem is related to many applications ranging from uncovering missing data to predicting the evolution of networks, link prediction has been intensively investigated recently and many methods have been proposed so...

Université de Fribourg

Prediction in complex systems: the case of the international trade network

Vidmer, Alexandre ; Zeng, An ; Medo, Matúš ; Zhang, Yi-Cheng

In: Physica A: Statistical Mechanics and its Applications, 2015, vol. 436, p. 188–199

Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the...

Université de Fribourg

Modeling mutual feedback between users and recommender systems

Zeng, An ; Yeung, Chi Ho ; Medo, Matúš ; Zhang, Yi-Cheng

In: Journal of Statistical Mechanics: Theory and Experiment, 2015, vol. 2015, no. 7, p. P07020

Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this...

Université de Fribourg

Reconstructing propagation networks with temporal similarity

Liao, Hao ; Zeng, An

In: Scientific Reports, 2015, vol. 5, p. 11404

Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a...