23,131 research outputs found
Systemic Risk in the Financial System: Insights From Network Science
Analyzes systemic risk from the perspective of network structure and the connectivity links between actors. Explores how the markets' lack of robustness, the pattern of network links, and the lack of diversity in networks contributed to the crisis
Long ties accelerate noisy threshold-based contagions
Network structure can affect when and how widely new ideas, products, and
behaviors are adopted. In widely-used models of biological contagion,
interventions that randomly rewire edges (generally making them "longer")
accelerate spread. However, there are other models relevant to social
contagion, such as those motivated by myopic best-response in games with
strategic complements, in which an individual's behavior is described by a
threshold number of adopting neighbors above which adoption occurs (i.e.,
complex contagions). Recent work has argued that highly clustered, rather than
random, networks facilitate spread of these complex contagions. Here we show
that minor modifications to this model, which make it more realistic, reverse
this result: we allow very rare below-threshold adoption, i.e., rarely adoption
occurs when there is only one adopting neighbor. To model the trade-off between
long and short edges we consider networks that are the union of cycle-power-
graphs and random graphs on nodes. Allowing adoptions below threshold to
occur with order probability along some "short" cycle edges is
enough to ensure that random rewiring accelerates spread. Simulations
illustrate the robustness of these results to other commonly-posited models for
noisy best-response behavior. Hypothetical interventions that randomly rewire
existing edges or add random edges (versus adding "short", triad-closing edges)
in hundreds of empirical social networks reduce time to spread. This revised
conclusion suggests that those wanting to increase spread should induce
formation of long ties, rather than triad-closing ties. More generally, this
highlights the importance of noise in game-theoretic analyses of behavior
The Lifecycle and Cascade of WeChat Social Messaging Groups
Social instant messaging services are emerging as a transformative form with
which people connect, communicate with friends in their daily life - they
catalyze the formation of social groups, and they bring people stronger sense
of community and connection. However, research community still knows little
about the formation and evolution of groups in the context of social messaging
- their lifecycles, the change in their underlying structures over time, and
the diffusion processes by which they develop new members. In this paper, we
analyze the daily usage logs from WeChat group messaging platform - the largest
standalone messaging communication service in China - with the goal of
understanding the processes by which social messaging groups come together,
grow new members, and evolve over time. Specifically, we discover a strong
dichotomy among groups in terms of their lifecycle, and develop a separability
model by taking into account a broad range of group-level features, showing
that long-term and short-term groups are inherently distinct. We also found
that the lifecycle of messaging groups is largely dependent on their social
roles and functions in users' daily social experiences and specific purposes.
Given the strong separability between the long-term and short-term groups, we
further address the problem concerning the early prediction of successful
communities. In addition to modeling the growth and evolution from group-level
perspective, we investigate the individual-level attributes of group members
and study the diffusion process by which groups gain new members. By
considering members' historical engagement behavior as well as the local social
network structure that they embedded in, we develop a membership cascade model
and demonstrate the effectiveness by achieving AUC of 95.31% in predicting
inviter, and an AUC of 98.66% in predicting invitee.Comment: 10 pages, 8 figures, to appear in proceedings of the 25th
International World Wide Web Conference (WWW 2016
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
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