109,156 research outputs found
Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge
This paper describes the winning entry to the IJCNN 2011 Social Network
Challenge run by Kaggle.com. The goal of the contest was to promote research on
real-world link prediction, and the dataset was a graph obtained by crawling
the popular Flickr social photo sharing website, with user identities scrubbed.
By de-anonymizing much of the competition test set using our own Flickr crawl,
we were able to effectively game the competition. Our attack represents a new
application of de-anonymization to gaming machine learning contests, suggesting
changes in how future competitions should be run.
We introduce a new simulated annealing-based weighted graph matching
algorithm for the seeding step of de-anonymization. We also show how to combine
de-anonymization with link prediction---the latter is required to achieve good
performance on the portion of the test set not de-anonymized---for example by
training the predictor on the de-anonymized portion of the test set, and
combining probabilistic predictions from de-anonymization and link prediction.Comment: 11 pages, 13 figures; submitted to IJCNN'201
A Latent Parameter Node-Centric Model for Spatial Networks
Spatial networks, in which nodes and edges are embedded in space, play a
vital role in the study of complex systems. For example, many social networks
attach geo-location information to each user, allowing the study of not only
topological interactions between users, but spatial interactions as well. The
defining property of spatial networks is that edge distances are associated
with a cost, which may subtly influence the topology of the network. However,
the cost function over distance is rarely known, thus developing a model of
connections in spatial networks is a difficult task.
In this paper, we introduce a novel model for capturing the interaction
between spatial effects and network structure. Our approach represents a unique
combination of ideas from latent variable statistical models and spatial
network modeling. In contrast to previous work, we view the ability to form
long/short-distance connections to be dependent on the individual nodes
involved. For example, a node's specific surroundings (e.g. network structure
and node density) may make it more likely to form a long distance link than
other nodes with the same degree. To capture this information, we attach a
latent variable to each node which represents a node's spatial reach. These
variables are inferred from the network structure using a Markov Chain Monte
Carlo algorithm.
We experimentally evaluate our proposed model on 4 different types of
real-world spatial networks (e.g. transportation, biological, infrastructure,
and social). We apply our model to the task of link prediction and achieve up
to a 35% improvement over previous approaches in terms of the area under the
ROC curve. Additionally, we show that our model is particularly helpful for
predicting links between nodes with low degrees. In these cases, we see much
larger improvements over previous models
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
Representation learning provides new and powerful graph analytical approaches
and tools for the highly valued data science challenge of mining knowledge
graphs. Since previous graph analytical methods have mostly focused on
homogeneous graphs, an important current challenge is extending this
methodology for richly heterogeneous graphs and knowledge domains. The
biomedical sciences are such a domain, reflecting the complexity of biology,
with entities such as genes, proteins, drugs, diseases, and phenotypes, and
relationships such as gene co-expression, biochemical regulation, and
biomolecular inhibition or activation. Therefore, the semantics of edges and
nodes are critical for representation learning and knowledge discovery in real
world biomedical problems. In this paper, we propose the edge2vec model, which
represents graphs considering edge semantics. An edge-type transition matrix is
trained by an Expectation-Maximization approach, and a stochastic gradient
descent model is employed to learn node embedding on a heterogeneous graph via
the trained transition matrix. edge2vec is validated on three biomedical domain
tasks: biomedical entity classification, compound-gene bioactivity prediction,
and biomedical information retrieval. Results show that by considering
edge-types into node embedding learning in heterogeneous graphs,
\textbf{edge2vec}\ significantly outperforms state-of-the-art models on all
three tasks. We propose this method for its added value relative to existing
graph analytical methodology, and in the real world context of biomedical
knowledge discovery applicability.Comment: 10 page
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