23,817 research outputs found
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Identifying communicator roles in Twitter
Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified?To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Characterizing Attention Cascades in WhatsApp Groups
An important political and social phenomena discussed in several countries,
like India and Brazil, is the use of WhatsApp to spread false or misleading
content. However, little is known about the information dissemination process
in WhatsApp groups. Attention affects the dissemination of information in
WhatsApp groups, determining what topics or subjects are more attractive to
participants of a group. In this paper, we characterize and analyze how
attention propagates among the participants of a WhatsApp group. An attention
cascade begins when a user asserts a topic in a message to the group, which
could include written text, photos, or links to articles online. Others then
propagate the information by responding to it. We analyzed attention cascades
in more than 1.7 million messages posted in 120 groups over one year. Our
analysis focused on the structural and temporal evolution of attention cascades
as well as on the behavior of users that participate in them. We found specific
characteristics in cascades associated with groups that discuss political
subjects and false information. For instance, we observe that cascades with
false information tend to be deeper, reach more users, and last longer in
political groups than in non-political groups.Comment: Accepted as a full paper at the 11th International ACM Web Science
Conference (WebSci 2019). Please cite the WebSci versio
Predicting links in ego-networks using temporal information
Link prediction appears as a central problem of network science, as it calls
for unfolding the mechanisms that govern the micro-dynamics of the network. In
this work, we are interested in ego-networks, that is the mere information of
interactions of a node to its neighbors, in the context of social
relationships. As the structural information is very poor, we rely on another
source of information to predict links among egos' neighbors: the timing of
interactions. We define several features to capture different kinds of temporal
information and apply machine learning methods to combine these various
features and improve the quality of the prediction. We demonstrate the
efficiency of this temporal approach on a cellphone interaction dataset,
pointing out features which prove themselves to perform well in this context,
in particular the temporal profile of interactions and elapsed time between
contacts.Comment: submitted to EPJ Data Scienc
Supersampling and network reconstruction of urban mobility
Understanding human mobility is of vital importance for urban planning,
epidemiology, and many other fields that aim to draw policies from the
activities of humans in space. Despite recent availability of large scale data
sets related to human mobility such as GPS traces, mobile phone data, etc., it
is still true that such data sets represent a subsample of the population of
interest, and then might give an incomplete picture of the entire population in
question. Notwithstanding the abundant usage of such inherently limited data
sets, the impact of sampling biases on mobility patterns is unclear -- we do
not have methods available to reliably infer mobility information from a
limited data set. Here, we investigate the effects of sampling using a data set
of millions of taxi movements in New York City. On the one hand, we show that
mobility patterns are highly stable once an appropriate simple rescaling is
applied to the data, implying negligible loss of information due to subsampling
over long time scales. On the other hand, contrasting an appropriate null model
on the weighted network of vehicle flows reveals distinctive features which
need to be accounted for. Accordingly, we formulate a "supersampling"
methodology which allows us to reliably extrapolate mobility data from a
reduced sample and propose a number of network-based metrics to reliably assess
its quality (and that of other human mobility models). Our approach provides a
well founded way to exploit temporal patterns to save effort in recording
mobility data, and opens the possibility to scale up data from limited records
when information on the full system is needed.Comment: 14 pages, 4 figure
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