13 research outputs found
Bursty egocentric network evolution in Skype
In this study we analyze the dynamics of the contact list evolution of
millions of users of the Skype communication network. We find that egocentric
networks evolve heterogeneously in time as events of edge additions and
deletions of individuals are grouped in long bursty clusters, which are
separated by long inactive periods. We classify users by their link creation
dynamics and show that bursty peaks of contact additions are likely to appear
shortly after user account creation. We also study possible relations between
bursty contact addition activity and other user-initiated actions like free and
paid service adoption events. We show that bursts of contact additions are
associated with increases in activity and adoption - an observation that can
inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013
Evolution of Ego-networks in Social Media with Link Recommendations
Ego-networks are fundamental structures in social graphs, yet the process of
their evolution is still widely unexplored. In an online context, a key
question is how link recommender systems may skew the growth of these networks,
possibly restraining diversity. To shed light on this matter, we analyze the
complete temporal evolution of 170M ego-networks extracted from Flickr and
Tumblr, comparing links that are created spontaneously with those that have
been algorithmically recommended. We find that the evolution of ego-networks is
bursty, community-driven, and characterized by subsequent phases of explosive
diameter increase, slight shrinking, and stabilization. Recommendations favor
popular and well-connected nodes, limiting the diameter expansion. With a
matching experiment aimed at detecting causal relationships from observational
data, we find that the bias introduced by the recommendations fosters global
diversity in the process of neighbor selection. Last, with two link prediction
experiments, we show how insights from our analysis can be used to improve the
effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl
Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts
Getting the overall picture of how a large number of ego-networks evolve is a
common yet challenging task. Existing techniques often require analysts to
inspect the evolution patterns of ego-networks one after another. In this
study, we explore an approach that allows analysts to interactively create
spatial layouts in which each dot is a dynamic ego-network. These spatial
layouts provide overviews of the evolution patterns of ego-networks, thereby
revealing different global patterns such as trends, clusters and outliers in
evolution patterns. To let analysts interactively construct interpretable
spatial layouts, we propose a data transformation pipeline, with which analysts
can adjust the spatial layouts and convert dynamic egonetworks into event
sequences to aid interpretations of the spatial positions. Based on this
transformation pipeline, we developed Segue, a visual analysis system that
supports thorough exploration of the evolution patterns of ego-networks.
Through two usage scenarios, we demonstrate how analysts can gain insights into
the overall evolution patterns of a large collection of ego-networks by
interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2018
Multidimensional human dynamics in mobile phone communications
In today's technology-assisted society, social interactions may be expressed through a variety of techno-communication channels, including online social networks, email and mobile phones (calls, text messages). Consequently, a clear grasp of human behavior through the diverse communication media is considered a key factor in understanding the formation of the today's information society. So far, all previous research on user communication behavior has focused on a sole communication activity. In this paper we move forward another step on this research path by performing a multidimensional study of human sociality as an expression of the use of mobile phones. The paper focuses on user temporal communication behavior in the interplay between the two complementary communication media, text messages and phone calls, that represent the bi-dimensional scenario of analysis. Our study provides a theoretical framework for analyzing multidimensional bursts as the most general burst category, that includes one-dimensional bursts as the simplest case, and offers empirical evidence of their nature by following the combined phone call/text message communication patterns of approximately one million people over three-month period. This quantitative approach enables the design of a generative model rooted in the three most significant features of the multidimensional burst - the number of dimensions, prevalence and interleaving degree - able to reproduce the main media usage attitude. The other findings of the paper include a novel multidimensional burst detection algorithm and an insight analysis of the human media selection process
Modern temporal network theory: A colloquium
The power of any kind of network approach lies in the ability to simplify a
complex system so that one can better understand its function as a whole.
Sometimes it is beneficial, however, to include more information than in a
simple graph of only nodes and links. Adding information about times of
interactions can make predictions and mechanistic understanding more accurate.
The drawback, however, is that there are not so many methods available, partly
because temporal networks is a relatively young field, partly because it more
difficult to develop such methods compared to for static networks. In this
colloquium, we review the methods to analyze and model temporal networks and
processes taking place on them, focusing mainly on the last three years. This
includes the spreading of infectious disease, opinions, rumors, in social
networks; information packets in computer networks; various types of signaling
in biology, and more. We also discuss future directions.Comment: Final accepted versio
FROM WIDE- TO SHORT-RANGE COMMUNICATIONS: USING HUMAN INTERACTIONS TO DESIGN NEW MOBILE SYSTEMS AND SERVICES
The widespread diffusion of mobile devices has radically changed the way people interact with each other and with object of their daily life. In particular, modern mobile devices are equipped with multiple radio interfaces allowing users to interact at different spatial granularities according to the various radio technology they use. The research community is progressively moving to heterogeneous network solutions which include many different wireless technologies seamlessly integrated to address a wide variety of use cases and requirements. In 5th- Generation (5G) of mobile network we can find multiple network typology such as device-to-device (D2D), vehicular networks, machine-to-machine(M2M), and more, which are integrated in the existing mobile-broadband technology such as LTE and its future evolutions. In this complex and rich scenario, many issues and challenges are still open from a technological, architectural, and mobile services and applications points of view. In this work we provide network solutions, mobile services, and applications consistent with the 5G mobile network vision by using users interactions as a common starting point. We focus on three different spatial granularities, long, medium/short, and micro mediated by cellular network, Wi-Fi, and NFC radio technologies, respectively. We deal with various kinds of issues and challenges according to the distinct spatial granularity we consider. We start with an user centric approach based on the analysis of the characteristics and the peculiarities of each kind of interaction. Following this path, we provide contributions to support the design of new network architectures, and the development of novel mobile services and applications
Local Information Diffusion Patterns in Social and Traditional Media: The Estonian Case Study
Paljud ettevõtted ja inimesed hindavad kõrgelt informatsiooni väärtust ja seda on eelkõige hakatud hindama viimase kümnekonna aasta jooksul. Tänu sellele on tekkinud ka huvi, kuidas info levib erinevates struktureeritud võrgustikes. Avaldatud on mitmeid teadustöid, mis uurivad informatsiooni levimist ühes reaalse elu võrgustikus nagu näiteks Facebooki postitused, Twitteri tweetid, Blogspoti blogikanded jne. Suuresti on need uurimused keskendunud ühele võrgustikule, mis ei hõlma kogu võrgu dünaamikat ja samuti välist mõju info levimisele. Samas on lähiminevikus avaldatud ka teadustöid, mis hõlmavad mitut erinevat võrgustiku ja analüüsivad välist mõju informatsiooni levimisele.
Käesoleva töö eesmärk on lähemalt uurida informatsiooni levimise mustreid võrgustikus, mis hõlmab erinevaid reaalelu võrgustike, kasutades selleks topoloogilisi ja aja mustreid. Topoloogiliste mustrite analüüsimiseks on kasutatud võrgustikus sagedalt levivate alamgraafide leidmise algoritme, aja mustreid uuritakse ajaseeriate klasterdamise teel. Töös kasutatud andmestik on kogutud Eesti uudismeediast - artiklid ja nende kommentaarid ning sotsiaalmeedia kanalitest, Twitterist ja Facebook-ist. Selle andmestiku põhjal loodi seosed eritüüpi andmeobjektide vahel, mille põhjal loodi võrgustik, mida kasutada edasiseks uurimiseks. Aja mustrid viitavad väga kiirele info levimisele antud võrgustikus, topoloogilised mustrid näitavad uudismeedia artiklite ja Facebook-i postituste suurt mõju info levimises. Töö tulemusi on võimalik rakendada küberkaitses, online turunduses ja kampaania haldamises, samuti ka mõjuvõimu hindamisel - kindlasti leiaks tulemused rakendust ka teistes valdkondades.Information has become more highly valued among companies and individuals than ever before. With this, the interest in how information diffuses among the entities in various structured networks has increased. A number of studies have been published on the diffusion process in real-life networks, such as web service network, citation networks, blog networks etc. Majority of researches have focused on one type of network - such as Facebook posts, Twitter tweets, Blogspot blog entries etc. A disadvantage of analysing a network containing entities from a single source is that it does not consider the outside influence on the diffusion. Recently, some papers have started to incorporate different networks in their study and as such have been able to analyse the effect of outside influence on the diffusion process.
This thesis aims to shed further light into the topic of information diffusion in a real world network containing entities from different sources, this is achieved by detection of relevant local topological and temporal information diffusion patterns. For topological pattern analysis, frequent subgraph mining techniques are used. Temporal patterns are extracted using time series clustering. The dataset used in this thesis is collected from the Estonian setting of mainstream online news media with comments and articles and from social media channels Twitter and Facebook. From this dataset the relations between the entities were extracted and a network for analysis of diffusion patterns was constructed. Temporal patterns reveal the high pace of information diffusion while topological patterns expose the important role of news media articles and Facebook posts in the information diffusion processes. The results of the thesis are applicable in cyber defence, online marketing and campaign management plus information impact estimation, just to mention a few application areas