Communication dynamics in finite capacity social networks

In communication networks structure and dynamics are tightly coupled. The structure controls the flow of information and is itself shaped by the dynamical process of information exchanged between nodes. In order to reconcile structure and dynamics, a generic model, based on the local interaction between nodes, is considered for the communication in large social networks. In agreement with data from a large human organization, we show that the flow is non-Markovian and controlled by the temporal limitations of individuals. We confirm the versatility of our model by predicting simultaneously the degree-dependent node activity, the balance between information input and output of nodes and the degree distribution. Finally, we quantify the limitations to network analysis when it is based on data sampled over a finite period of time.Comment: Physical Review Letter, accepted (5 pages, 4 figures

Dynamics in online social networks

An increasing number of today's social interactions occurs using online social media as communication channels. Some online social networks have become extremely popular in the last decade. They differ among themselves in the character of the service they provide to online users. For instance, Facebook can be seen mainly as a platform for keeping in touch with close friends and relatives, Twitter is used to propagate and receive news, LinkedIn facilitates the maintenance of professional contacts, Flickr gathers amateurs and professionals of photography, etc. Albeit different, all these online platforms share an ingredient that pervades all their applications. There exists an underlying social network that allows their users to keep in touch with each other and helps to engage them in common activities or interactions leading to a better fulfillment of the service's purposes. This is the reason why these platforms share a good number of functionalities, e.g., personal communication channels, broadcasted status updates, easy one-step information sharing, news feeds exposing broadcasted content, etc. As a result, online social networks are an interesting field to study an online social behavior that seems to be generic among the different online services. Since at the bottom of these services lays a network of declared relations and the basic interactions in these platforms tend to be pairwise, a natural methodology for studying these systems is provided by network science. In this chapter we describe some of the results of research studies on the structure, dynamics and social activity in online social networks. We present them in the interdisciplinary context of network science, sociological studies and computer science.Comment: 17 pages, 4 figures, book chapte

Reliable online social network data collection

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin

Towards validation of the Internet Census 2012

The reliability of the ``Internet Census 2012'' (IC), an anonymously published scan of the entire IPv4 address space, is not a priori clear. As a step towards validation of this dataset, we compare it to logged reference data on a /16 network, and present an approach to systematically handle uncertainties in timestamps in the IC and reference data. We find evidence the scan indeed took place, and a 93\% match with the /16 reference data

Speedtrap: Internet-Scale IPv6 Alias Resolution

Proceedings of the Thirteenth ACM SIGCOMM Internet Measurement (IMC 2013) Conference, Barcelona, ES, October 2013.The article of record as published may be located at http://dx.doi.org/10.1145/2504730.2504759.Impediments to resolving IPv6 router aliases have precluded understanding the emerging router-level IPv6 Internet topology. In this work, we design, implement, and validate the first {\em Internet-scale alias resolution technique} for IPv6. Our technique, \st, leverages the ability to induce fragmented IPv6 responses from router interfaces in a particular temporal pattern that produces distinguishing per-router fingerprints. Our algorithm surmounts three fundamental challenges to Internet-scale IPv6 alias resolution using fragment identifier values: (1) unlike for IPv4, the identifier counters on IPv6 routers have no natural velocity, (2) the values of these counters are similar across routers, and (3) the packet size required to collect inferences is 46 times larger than required in IPv4. We demonstrate the efficacy of the technique by producing router-level Internet IPv6 topologies using measurements from CAIDA's distributed infrastructure. Our preliminary work represents a step toward understanding the Internet's IPv6 router-level topology, an important objective with respect to IPv6 network resilience, security, policy, and longitudinal evolution

Scanning the internet for liveness

Internet-wide scanning depends on a notion of liveness: does a target IP address respond to a probe packet? However, the interpretation of such responses, or lack of them, is nuanced and depends on multiple factors, including: how we probed, how different protocols in the network stack interact, the presence of filtering policies near the target, and temporal churn in IP responsiveness. Although often neglected, these factors can significantly affect the results of active measurement studies. We develop a taxonomy of liveness which we employ to develop a method to perform concurrent IPv4 scans using ICMP, five TCP-based, and two UDP-based protocols, comprehensively capturing all responses to our probes, including negative and cross-layer responses. Leveraging our methodology, we present a systematic analysis of liveness and how it manifests in active scanning campaigns, yielding practical insights and methodological improvements for the design and the execution of active Internet measurement studies.</jats:p

Damage detection via shortest-path network sampling

Large networked systems are constantly exposed to local damages and failures that can alter their functionality. The knowledge of the structure of these systems is, however, often derived through sampling strategies whose effectiveness at damage detection has not been thoroughly investigated so far. Here, we study the performance of shortest-path sampling for damage detection in large-scale networks. We define appropriate metrics to characterize the sampling process before and after the damage, providing statistical estimates for the status of nodes (damaged, not damaged). The proposed methodology is flexible and allows tuning the trade-off between the accuracy of the damage detection and the number of probes used to sample the network. We test and measure the efficiency of our approach considering both synthetic and real networks data. Remarkably, in all of the systems studied, the number of correctly identified damaged nodes exceeds the number of false positives, allowing us to uncover the damage precisely

Study of Evolution Model of China Education and Research Network

By searching the hyperlinks with domain name “.edu.cn” which constitutes the China Education and Research Network, we build a complex directed network containing 366,422 web pages containing 540,755 URLs. These URLs constitute a complex directed network through self-organization. By analyzing the topology of China Education and Research Network, we found that it is different from the common Internet in several aspects. Most of the vertices have incoming links, a few vertices have outgoing links, and very few vertices have both incoming and outgoing links. The vertex distribution has a power-law tail. A large proportion of newly added edges always connect with those pages selected from one subnetwork that they belong to, instead of connecting with the pages selected from the whole network. According to these features, we presented the evolution model of this complex directed network. The results indicate that this model reflects some main characteristics of China Education and Research Network