arXiv subjects

Jichang Zhao

Publications and source records attributed to Jichang Zhao.

46 records · Page 3Linked to original sources

arXiv2014

Weighted Shortest Path Models: A Revisit to the Simulation of Internet Routing

Understanding how packets are routed in Internet is significantly important to Internet measurement and modeling. The conventional solution for route simulation is based on the assumption of unweighted shortest path. However, it has been found and widely accepted that a packet in Internet is usually not transmitted along the unweighted shortest path between its source and destination. To better simulate the routing behavior of a packet, we thoroughly explore the real-world Internet routes and present a novel local information based simulation model, with a tuning parameter, which assigns weights to links based on local information and then simulates the Internet route with weighted shortest path. Comparisons with baseline approaches show its capability in well replicating the route length distribution and other structural properties of the Internet topology. Meanwhile, the optimal parameter of this model locates in the range of (0, 2), which implies that a packet inside the Internet inclines to move to nodes with small degrees. This behavior actually reflects the design philosophy of Internet routing, balancing between network efficiency and traffic congestion.

cs.NI↗

arXiv2014

Who creates trends in online social media: The crowd or opinion leaders?

Trends in online social media always reflect the collective attention of a vast number of individuals across the network. For example, Internet slang words can be ubiquitous because of social memes and online contagions in an extremely short period. From Weibo, a Twitter-like service in China, we find that the adoption of popular Internet slang words experiences two peaks in its temporal evolution, in which the former is relatively much lower than the latter. This interesting phenomenon in fact provides a decent window to disclose essential factors that drive the massive diffusion underlying trends in online social media. Specifically, the in-depth comparison between diffusions represented by different peaks suggests that more attention from the crowd at early stage of the propagation produces large-scale coverage, while the dominant participation of opinion leaders at the early stage just leads to popularity of small scope. Our results quantificationally challenge the conventional hypothesis of influentials. And the implications of these novel findings for marketing practice and influence maximization in social networks are also discussed.

cs.SI↗

arXiv2014

A universal law in human mobility

The intrinsic factor that drives the human movement remains unclear for decades. While our observations from intra-urban and inter-urban trips both demonstrate a universal law in human mobility. Be specific, the probability from one location to another is inversely proportional to the number of population living in locations which are closer than the destination. A simple rank-based model is then presented, which is parameterless but predicts human flows with a convincing fidelity. Besides, comparison with other models shows that our model is more stable and fundamental at different spatial scales by implying the strong correlation between human mobility and social relationship.

physics.soc-ph↗

arXiv2013

Anger is More Influential Than Joy: Sentiment Correlation in Weibo

Recent years have witnessed the tremendous growth of the online social media. In China, Weibo, a Twitter-like service, has attracted more than 500 million users in less than four years. Connected by online social ties, different users influence each other emotionally. We find the correlation of anger among users is significantly higher than that of joy, which indicates that angry emotion could spread more quickly and broadly in the network. While the correlation of sadness is surprisingly low and highly fluctuated. Moreover, there is a stronger sentiment correlation between a pair of users if they share more interactions. And users with larger number of friends posses more significant sentiment influence to their neighborhoods. Our findings could provide insights for modeling sentiment influence and propagation in online social networks.

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arXiv2012

Modeling collective human mobility: Understanding exponential law of intra-urban movement

It is very important to understand urban mobility patterns because most trips are concentrated in urban areas. In the paper, a new model is proposed to model collective human mobility in urban areas. The model can be applied to predict individual flows not only in intra-city but also in countries or a larger range. Based on the model, it can be concluded that the exponential law of distance distribution is attributed to decreasing exponentially of average density of human travel demands. Since the distribution of human travel demands only depends on urban planning, population distribution, regional functions and so on, it illustrates that these inherent properties of cities are impetus to drive collective human movements.

physics.soc-ph↗

arXiv2011

Performance of Local Information Based Link Prediction: A Sampling Perspective

Link prediction is pervasively employed to uncover the missing links in the snapshots of real-world networks, which are usually obtained from kinds of sampling methods. Contrarily, in the previous literature, in order to evaluate the performance of the prediction, the known edges in the sampled snapshot are divided into the training set and the probe set randomly, without considering the diverse sampling approaches beyond. However, different sampling methods might lead to different missing links, especially for the biased ones. For this reason, random partition based evaluation of performance is no longer convincing if we take the sampling method into account. Hence, in this paper, aim at filling this void, we try to reevaluate the performance of local information based link predictions through sampling methods governed division of the training set and the probe set. It is interesting that we find for different sampling methods, each prediction approach performs unevenly. Moreover, most of these predictions perform weakly when the sampling method is biased, which indicates that the performance of these methods is overestimated in the prior works.

cs.SI↗

arXiv2011

Link Prediction in Complex Networks: A Clustering Perspective

Link prediction is an open problem in the complex network, which attracts much research interest currently. However, little attention has been paid to the relation between network structure and the performance of prediction methods. In order to fill this vital gap, we try to understand how the network structure affects the performance of link prediction methods in the view of clustering. Our experiments on both synthetic and real-world networks show that as the clustering grows, the precision of these methods could be improved remarkably, while for the sparse and weakly clustered network, they perform poorly. We explain this through the distinguishment caused by increased clustering between the score distribution of positive and negative instances. Our finding also sheds light on the problem of how to select appropriate approaches for different networks with various densities and clusterings.

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arXiv2010

Weak ties: Subtle role of information diffusion in online social networks

As a social media, online social networks play a vital role in the social information diffusion. However, due to its unique complexity, the mechanism of the diffusion in online social networks is different from the ones in other types of networks and remains unclear to us. Meanwhile, few works have been done to reveal the coupled dynamics of both the structure and the diffusion of online social networks. To this end, in this paper, we propose a model to investigate how the structure is coupled with the diffusion in online social networks from the view of weak ties. Through numerical experiments on large-scale online social networks, we find that in contrast to some previous research results, selecting weak ties preferentially to republish cannot make the information diffuse quickly, while random selection can achieve this goal. However, when we remove the weak ties gradually, the coverage of the information will drop sharply even in the case of random selection. We also give a reasonable explanation for this by extra analysis and experiments. Finally, we conclude that weak ties play a subtle role in the information diffusion in online social networks. On one hand, they act as bridges to connect isolated local communities together and break through the local trapping of the information. On the other hand, selecting them as preferential paths to republish cannot help the information spread further in the network. As a result, weak ties might be of use in the control of the virus spread and the private information diffusion in real-world applications.

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arXiv2010

Being Rational or Aggressive? A Revisit to Dunbar's Number in Online Social Networks

Recent years have witnessed the explosion of online social networks (OSNs). They provide powerful IT-innovations for online social activities such as organizing contacts, publishing contents, and sharing interests between friends who may never meet before. As more and more people become the active users of online social networks, one may ponder questions such as: (1) Do OSNs indeed improve our sociability? (2) To what extent can we expand our offline social spectrum in OSNs? (3) Can we identify some interesting user behaviors in OSNs? Our work in this paper just aims to answer these interesting questions. To this end, we pay a revisit to the well-known Dunbar's number in online social networks. Our main research contributions are as follows. First, to our best knowledge, our work is the first one that systematically validates the existence of the online Dunbar's number in the range of [200,300]. To reach this, we combine using local-structure analysis and user-interaction analysis for extensive real-world OSNs. Second, we divide OSNs users into two categories: rational and aggressive, and find that rational users intend to develop close and reciprocated relationships, whereas aggressive users have no consistent behaviors. Third, we build a simple model to capture the constraints of time and cognition that affect the evolution of online social networks. Finally, we show the potential use of our findings in viral marketing and privacy management in online social networks.

cs.SI↗

arXiv2009

Enhancing the robustness of scale-free networks

Error tolerance and attack vulnerability are two common and important properties of complex networks, which are usually used to evaluate the robustness of a network. Recently, much work has been devoted to determining the network design with optimal robustness. However, little attention has been paid to the problem of how to improve the robustness of existing networks. In this paper, we present a new parameter alpha, called enforcing parameter, to guide the process of enhancing the robustness of scale-free networks by gradually adding new links. Intuitively, alpha < 0 means the nodes with lower degrees are selected preferentially while the nodes with higher degrees will be more probably selected when alpha > 0. It is shown both theoretically and experimentally that when alpha < 0 the attack survivability of the network can be enforced apparently. Then we propose new strategies to enhance the network robustness. Through extensive experiments and comparisons, we conclude that establishing new links between nodes with low degrees can drastically enforce the attack survivability of scale-free networks while having little impact on the error tolerance.

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