5,968 research outputs found
Leveraging Friendship Networks for Dynamic Link Prediction in Social Interaction Networks
On-line social networks (OSNs) often contain many different types of
relationships between users. When studying the structure of OSNs such as
Facebook, two of the most commonly studied networks are friendship and
interaction networks. The link prediction problem in friendship networks has
been heavily studied. There has also been prior work on link prediction in
interaction networks, independent of friendship networks. In this paper, we
study the predictive power of combining friendship and interaction networks. We
hypothesize that, by leveraging friendship networks, we can improve the
accuracy of link prediction in interaction networks. We augment several
interaction link prediction algorithms to incorporate friendships and predicted
friendships. From experiments on Facebook data, we find that incorporating
friendships into interaction link prediction algorithms results in higher
accuracy, but incorporating predicted friendships does not when compared to
incorporating current friendships.Comment: To appear in ICWSM 2018. This version corrects some minor errors in
Table 1. MATLAB code available at
https://github.com/IdeasLabUT/Friendship-Interaction-Predictio
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
Predicting link directions via a recursive subgraph-based ranking
Link directions are essential to the functionality of networks and their
prediction is helpful towards a better knowledge of directed networks from
incomplete real-world data. We study the problem of predicting the directions
of some links by using the existence and directions of the rest of links. We
propose a solution by first ranking nodes in a specific order and then
predicting each link as stemming from a lower-ranked node towards a
higher-ranked one. The proposed ranking method works recursively by utilizing
local indicators on multiple scales, each corresponding to a subgraph extracted
from the original network. Experiments on real networks show that the
directions of a substantial fraction of links can be correctly recovered by our
method, which outperforms either purely local or global methods.Comment: 6 pages, 5 figures; revised arguments for methods section; figures
replotted; minor revision
Principled Multilayer Network Embedding
Multilayer network analysis has become a vital tool for understanding
different relationships and their interactions in a complex system, where each
layer in a multilayer network depicts the topological structure of a group of
nodes corresponding to a particular relationship. The interactions among
different layers imply how the interplay of different relations on the topology
of each layer. For a single-layer network, network embedding methods have been
proposed to project the nodes in a network into a continuous vector space with
a relatively small number of dimensions, where the space embeds the social
representations among nodes. These algorithms have been proved to have a better
performance on a variety of regular graph analysis tasks, such as link
prediction, or multi-label classification. In this paper, by extending a
standard graph mining into multilayer network, we have proposed three methods
("network aggregation," "results aggregation" and "layer co-analysis") to
project a multilayer network into a continuous vector space. From the
evaluation, we have proved that comparing with regular link prediction methods,
"layer co-analysis" achieved the best performance on most of the datasets,
while "network aggregation" and "results aggregation" also have better
performance than regular link prediction methods
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
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