Towards the characterization of individual users through Web analytics

We perform an analysis of the way individual users navigate in the Web. We focus primarily in the temporal patterns of they return to a given page. The return probability as a function of time as well as the distribution of time intervals between consecutive visits are measured and found to be independent of the level of activity of single users. The results indicate a rich variety of individual behaviors and seem to preclude the possibility of defining a characteristic frequency for each user in his/her visits to a single site.Comment: 8 pages, 4 figures. To appear in Proceeding of Complex'0

The Next Paradigm Shift in the Mobile Ecosystem: Mobile Social Computing and the Increasing Relevance of Users

Social computing has become the paradigm for the increasingly relevant role of users in the Internet world. In this paper, it is argued that mobile social computing will eventually cause an even bigger impact in the mobile ecosystem. We are already at the beginning of the "transference" of a significant part of Internet social computing usage to the mobile domain, where users are no longer passive consumers of content andapplications, but co-creators and even innovators of them. However, mobile social computing will go one step further in the contribution to the development of the mobile ecosystem, since it will put the many situations of users' daily activities at the centre stage. To prove this case, this paper gathers available data and evidence on the patterns of mobile social computing usage and discusses user innovation and user empowerment in the framework of the current mobile ecosystem.Mobile social computing, user innovation, mobile ecosystem.

Scale-Free Phenomena in Communication Networks: A Cross-Atlantic Comparison

?Small-world networks? have a high degree of local clustering or cliqueness, like a regular lattice and a relatively short average minimum path, like a completely random network. The huge appeal of ?small-world networks? lies in the impact they are said to have on dynamical systems. In a transportation network, ?small-world? topology could improve the flow of people or goods through the network, which has important implications for the design of such networks. Preliminary research has shown that ?small-world network? phenomenon can arise in traffic networks possessing ?small-world? network topology (i.e., in a network that has a structure somewhere in between a regular lattice and random graph) and that, at least under certain circumstances, traffic appears to flow more efficiently through a network with such topology (Schintler and Kulkarni, 2000). This paper will explore this further through simulation under varying assumptions regarding the size of the network (i.e., in terms of number of nodes and edges), the level of traffic in the network, the uniformity of nodes and edges and the information levels of travelers in the network. The simulations will be done using the random rewiring process introduced by Watts and Strogatz (1998), where each time the network is rewired, the distribution of traffic and congestion through the network, and the ?small-world? network parameters, shortest average minimum path and clustering coefficient, will be examined. Traffic flow will be estimated using a gravity model framework and a route choice optimization program. The simulations will also be used to reveal whether or not there are certain nodes or links that suffer at the expense of the entire network becoming more efficient. In addition, the possibility of a self-organised criticality (SOC) structure will be examined. The concept, introduced by Bak et al.,(1987), gained a great deal of attention in past decades for its capability to explore the significant and structural transformation of a dynamic system. SOC sets out how prominent exogenous forces together with strong localized interactions at the micro level lead a system to a critical state at the macro-level. A further step in our analysis is the investigation of whether a power-law distribution, characteristic of the SOC state, evolves in the traffic network. While ?small-world? network topology may be shown to improve the efficiency of traffic flow through a network, it should be recognized that ?small-world? networks are sparse by nature. The shut down or major disruption of any link in such a network, particularly one with heavy congestion, could provoke significant disorder. This paper will also explore the effect that disruptions of this nature have on networks designed with a high degree of local clustering and a short average minimum path. The fact that a ?small-world? network is sparse also raises other issues for the transportation planner. If ?small-world? topology is in fact a desirable property for transportation networks, how do we transform existing networks to produce these results? Unlike other networks, such as those for telecommunications or socialization, a transportation network cannot be rewired to achieve a more efficient network structure. This issue will also be addressed in the paper. REFERENCES Bak, P., C. Tang, and K. Wiesenfeld (1987), ?Self-Organised Criticality?, Physical Review Letters, Vol. 59 (4), pp. 381-384. Watts, D.J. and S.H. Strogatz (1998). ?Collective Dynamics of ?Small-World? Networks? Nature, Vol 393, 4, pp. 440-442. Schintler, L.A. and R. Kulkarni (2000). ?The Emergence of Small-World Phenonmenon in Urban Transportation Networks? in Reggiani, A. (ed.), Spatial Economic Science: New Frontiers in Theory and Methodology, Springer-Verlag, Berlin-NewYork, pp. 419-434.

Profiling user activities with minimal traffic traces

Understanding user behavior is essential to personalize and enrich a user's online experience. While there are significant benefits to be accrued from the pursuit of personalized services based on a fine-grained behavioral analysis, care must be taken to address user privacy concerns. In this paper, we consider the use of web traces with truncated URLs - each URL is trimmed to only contain the web domain - for this purpose. While such truncation removes the fine-grained sensitive information, it also strips the data of many features that are crucial to the profiling of user activity. We show how to overcome the severe handicap of lack of crucial features for the purpose of filtering out the URLs representing a user activity from the noisy network traffic trace (including advertisement, spam, analytics, webscripts) with high accuracy. This activity profiling with truncated URLs enables the network operators to provide personalized services while mitigating privacy concerns by storing and sharing only truncated traffic traces. In order to offset the accuracy loss due to truncation, our statistical methodology leverages specialized features extracted from a group of consecutive URLs that represent a micro user action like web click, chat reply, etc., which we call bursts. These bursts, in turn, are detected by a novel algorithm which is based on our observed characteristics of the inter-arrival time of HTTP records. We present an extensive experimental evaluation on a real dataset of mobile web traces, consisting of more than 130 million records, representing the browsing activities of 10,000 users over a period of 30 days. Our results show that the proposed methodology achieves around 90% accuracy in segregating URLs representing user activities from non-representative URLs

Exploring universal patterns in human home-work commuting from mobile phone data

Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country and city scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)--despite substantial spatial and infrastructural differences. Furthermore, a comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors--as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance