35,002 research outputs found
Searching for superspreaders of information in real-world social media
A number of predictors have been suggested to detect the most influential
spreaders of information in online social media across various domains such as
Twitter or Facebook. In particular, degree, PageRank, k-core and other
centralities have been adopted to rank the spreading capability of users in
information dissemination media. So far, validation of the proposed predictors
has been done by simulating the spreading dynamics rather than following real
information flow in social networks. Consequently, only model-dependent
contradictory results have been achieved so far for the best predictor. Here,
we address this issue directly. We search for influential spreaders by
following the real spreading dynamics in a wide range of networks. We find that
the widely-used degree and PageRank fail in ranking users' influence. We find
that the best spreaders are consistently located in the k-core across
dissimilar social platforms such as Twitter, Facebook, Livejournal and
scientific publishing in the American Physical Society. Furthermore, when the
complete global network structure is unavailable, we find that the sum of the
nearest neighbors' degree is a reliable local proxy for user's influence. Our
analysis provides practical instructions for optimal design of strategies for
"viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure
Efficient detection of contagious outbreaks in massive metropolitan encounter networks
Physical contact remains difficult to trace in large metropolitan networks,
though it is a key vehicle for the transmission of contagious outbreaks.
Co-presence encounters during daily transit use provide us with a city-scale
time-resolved physical contact network, consisting of 1 billion contacts among
3 million transit users. Here, we study the advantage that knowledge of such
co-presence structures may provide for early detection of contagious outbreaks.
We first examine the "friend sensor" scheme --- a simple, but universal
strategy requiring only local information --- and demonstrate that it provides
significant early detection of simulated outbreaks. Taking advantage of the
full network structure, we then identify advanced "global sensor sets",
obtaining substantial early warning times savings over the friends sensor
scheme. Individuals with highest number of encounters are the most efficient
sensors, with performance comparable to individuals with the highest travel
frequency, exploratory behavior and structural centrality. An efficiency
balance emerges when testing the dependency on sensor size and evaluating
sensor reliability; we find that substantial and reliable lead-time could be
attained by monitoring only 0.01% of the population with the highest degree.Comment: 4 figure
Analysis of a large-scale weighted network of one-to-one human communication
We construct a connected network of 3.9 million nodes from mobile phone call
records, which can be regarded as a proxy for the underlying human
communication network at the societal level. We assign two weights on each edge
to reflect the strength of social interaction, which are the aggregate call
duration and the cumulative number of calls placed between the individuals over
a period of 18 weeks. We present a detailed analysis of this weighted network
by examining its degree, strength, and weight distributions, as well as its
topological assortativity and weighted assortativity, clustering and weighted
clustering, together with correlations between these quantities. We give an
account of motif intensity and coherence distributions and compare them to a
randomized reference system. We also use the concept of link overlap to measure
the number of common neighbors any two adjacent nodes have, which serves as a
useful local measure for identifying the interconnectedness of communities. We
report a positive correlation between the overlap and weight of a link, thus
providing strong quantitative evidence for the weak ties hypothesis, a central
concept in social network analysis. The percolation properties of the network
are found to depend on the type and order of removed links, and they can help
understand how the local structure of the network manifests itself at the
global level. We hope that our results will contribute to modeling weighted
large-scale social networks, and believe that the systematic approach followed
here can be adopted to study other weighted networks.Comment: 25 pages, 17 figures, 2 table
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Academics’ online connections: Characterising the structure of personal networks on academic social networking sites and Twitter
Academic social networking sites (SNS), such as Academia.edu and ResearchGate, seek to bring the benefits of online social networking to academics' professional lives. Online academic social networking offers the potential to revolutionise academic publishing, foster novel collaborations, and empower academics to develop their professional identities online. However, the role that such sites play in relation to academic practice and other social media is not well understood at present.
Arguably, the defining characteristic of academic social networking sites is the connections formed between profiles (in contrast to the traditional static academic homepage, for example). The social network of connections fostered by SNSs occupies an interesting space in relation to online identity, being both an attribute of an individual and shaped by the social context they are embedded within. As such, personal network structures may reflect an expression of identity (as "public displays of connection" (Donath & boyd, 2004) or "relational self portraits[s]" (Hogan & Wellman, 2014)), while social capital has been linked to network structures (Crossley et al., 2015). Network structure may therefore have implications for the types of roles that a network can play in professional life. What types of network structures are being fostered by academic SNS and how do they relate to academics' development of an online identity?
This presentation will discuss findings from a project which has used a mixed-methods social network analysis approach to analyse academics' personal networks online. The personal networks of 55 academics (sampled from survey participants, to reflect a range of disciplines and job positions) on both one academic SNS (either Academia.edu or ResearchGate) and Twitter were collected and analysed. Differences in network structure emerged according to platform, with Twitter networks being larger and less dense, while academic SNS networks were smaller and more highly clustered. There were differences between academic SNS and Twitter in the brokerage positions occupied by the participant. The results are discussed in relation to other salient studies relating network structure in online social networks to social capital, and implications for academic practice. Future work, including co-interpretive interviews to explore the significance of network structures with participants, is introduced
Extraction and Analysis of Facebook Friendship Relations
Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud
However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms
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