185,080 research outputs found
Max-linear models on infinte graphs generated by Bernoulli bond percolation
We extend previous work of max-linear models on finite directed acyclic
graphs to infinite graphs, and investigate their relations to classical
percolation theory. We formulate results for the oriented square lattice graph
and nearest neighbor bond percolation. Focus is on the
dependence introduced by this graph into the max-linear model. As a natural
application we consider communication networks, in particular, the distribution
of extreme opinions in social networks.Comment: 18 page
Batch kernel SOM and related Laplacian methods for social network analysis
Large graphs are natural mathematical models for describing the structure of
the data in a wide variety of fields, such as web mining, social networks,
information retrieval, biological networks, etc. For all these applications,
automatic tools are required to get a synthetic view of the graph and to reach
a good understanding of the underlying problem. In particular, discovering
groups of tightly connected vertices and understanding the relations between
those groups is very important in practice. This paper shows how a kernel
version of the batch Self Organizing Map can be used to achieve these goals via
kernels derived from the Laplacian matrix of the graph, especially when it is
used in conjunction with more classical methods based on the spectral analysis
of the graph. The proposed method is used to explore the structure of a
medieval social network modeled through a weighted graph that has been directly
built from a large corpus of agrarian contracts
Exponential-family Random Network Models
Random graphs, where the connections between nodes are considered random
variables, have wide applicability in the social sciences. Exponential-family
Random Graph Models (ERGM) have shown themselves to be a useful class of models
for representing com- plex social phenomena. We generalize ERGM by also
modeling nodal attributes as random variates, thus creating a random model of
the full network, which we call Exponential-family Random Network Models
(ERNM). We demonstrate how this framework allows a new formu- lation for
logistic regression in network data. We develop likelihood-based inference for
the model and an MCMC algorithm to implement it. This new model formulation is
used to analyze a peer social network from the National Lon- gitudinal Study of
Adolescent Health. We model the relationship between substance use and
friendship relations, and show how the results differ from the standard use of
logistic regression on network data
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
With the rapid expansion of mobile phone networks in developing countries,
large-scale graph machine learning has gained sudden relevance in the study of
global poverty. Recent applications range from humanitarian response and
poverty estimation to urban planning and epidemic containment. Yet the vast
majority of computational tools and algorithms used in these applications do
not account for the multi-view nature of social networks: people are related in
myriad ways, but most graph learning models treat relations as binary. In this
paper, we develop a graph-based convolutional network for learning on
multi-view networks. We show that this method outperforms state-of-the-art
semi-supervised learning algorithms on three different prediction tasks using
mobile phone datasets from three different developing countries. We also show
that, while designed specifically for use in poverty research, the algorithm
also outperforms existing benchmarks on a broader set of learning tasks on
multi-view networks, including node labelling in citation networks
Model Checking Social Network Models
A social network service is a platform to build social relations among people
sharing similar interests and activities. The underlying structure of a social
networks service is the social graph, where nodes represent users and the arcs
represent the users' social links and other kind of connections. One important
concern in social networks is privacy: what others are (not) allowed to know
about us. The "logic of knowledge" (epistemic logic) is thus a good formalism
to define, and reason about, privacy policies. In this paper we consider the
problem of verifying knowledge properties over social network models (SNMs),
that is social graphs enriched with knowledge bases containing the information
that the users know. More concretely, our contributions are: i) We prove that
the model checking problem for epistemic properties over SNMs is decidable; ii)
We prove that a number of properties of knowledge that are sound w.r.t. Kripke
models are also sound w.r.t. SNMs; iii) We give a satisfaction-preserving
encoding of SNMs into canonical Kripke models, and we also characterise which
Kripke models may be translated into SNMs; iv) We show that, for SNMs, the
model checking problem is cheaper than the one based on standard Kripke models.
Finally, we have developed a proof-of-concept implementation of the
model-checking algorithm for SNMs.Comment: In Proceedings GandALF 2017, arXiv:1709.0176
Modeling social networks from sampled data
Network models are widely used to represent relational information among
interacting units and the structural implications of these relations. Recently,
social network studies have focused a great deal of attention on random graph
models of networks whose nodes represent individual social actors and whose
edges represent a specified relationship between the actors. Most inference for
social network models assumes that the presence or absence of all possible
links is observed, that the information is completely reliable, and that there
are no measurement (e.g., recording) errors. This is clearly not true in
practice, as much network data is collected though sample surveys. In addition
even if a census of a population is attempted, individuals and links between
individuals are missed (i.e., do not appear in the recorded data). In this
paper we develop the conceptual and computational theory for inference based on
sampled network information. We first review forms of network sampling designs
used in practice. We consider inference from the likelihood framework, and
develop a typology of network data that reflects their treatment within this
frame. We then develop inference for social network models based on information
from adaptive network designs. We motivate and illustrate these ideas by
analyzing the effect of link-tracing sampling designs on a collaboration
network.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS221 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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