4 research outputs found
Handling oversampling in dynamic networks using link prediction
Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.Comment: ECML/PKDD 201
The power of implicit social relation in rating prediction of social recommender systems of social recommender
The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy
FAST LEARNING ON GRAPHS
We carry out a systematic study of classification problems on networked data,
presenting novel techniques with good performance both in theory and in
practice.
We assess the power of node classification based on class-linkage information
only. In particular, we propose four new algorithms that exploit the
homiphilic bias (linked entities tend to belong to the same class) in different
ways.
The set of the algorithms we present covers diverse practical needs: some
of them operate in an active transductive setting and others in an on-line
transductive setting. A third group works within an explorative protocol,
in which the vertices of an unknown graph are progressively revealed to the
learner in an on-line fashion.
Within the mistake bound learning model, for each of our algorithms
we provide a rigorous theoretical analysis, together with an interpretation
of the obtained performance bounds. We also design adversarial strategies
achieving matching lower bounds. In particular, we prove optimality for all
input graphs and for all fixed regularity values of suitable labeling complexity
measures. We also analyze the computational requirements of our methods,
showing that our algorithms can to handle very large data sets.
In the case of the on-line protocol, for which we exhibit an optimal algorithm
with constant amortized time per prediction, we validate our theoretical
results carrying out experiments on real-world datasets