69 research outputs found
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Since the label collecting is prohibitive and time-consuming, unsupervised
methods are preferred in applications such as fraud detection. Meanwhile, such
applications usually require modeling the intrinsic clusters in
high-dimensional data, which usually displays heterogeneous statistical
patterns as the patterns of different clusters may appear in different
dimensions. Existing methods propose to model the data clusters on selected
dimensions, yet globally omitting any dimension may damage the pattern of
certain clusters. To address the above issues, we propose a novel unsupervised
generative framework called FIRD, which utilizes adversarial distributions to
fit and disentangle the heterogeneous statistical patterns. When applying to
discrete spaces, FIRD effectively distinguishes the synchronized fraudsters
from normal users. Besides, FIRD also provides superior performance on anomaly
detection datasets compared with SOTA anomaly detection methods (over 5%
average AUC improvement). The significant experiment results on various
datasets verify that the proposed method can better model the heterogeneous
statistical patterns in high-dimensional data and benefit downstream
applications
Combined node and link partitions method for finding overlapping communities in complex networks
Community detection in complex networks is a fundamental data analysis task in various domains, and how to effectively find overlapping communities in real applications is still a challenge. In this work, we propose a new unified model and method for finding the best overlapping communities on the basis of the associated node and link partitions derived from the same framework. Specifically, we first describe a unified model that accommodates node and link communities (partitions) together, and then present a nonnegative matrix factorization method to learn the parameters of the model. Thereafter, we infer the overlapping communities based on the derived node and link communities, i.e., determine each overlapped community between the corresponding node and link community with a greedy optimization of a local community function conductance. Finally, we introduce a model selection method based on consensus clustering to determine the number of communities. We have evaluated our method on both synthetic and real-world networks with ground-truths, and compared it with seven state-of-the-art methods. The experimental results demonstrate the superior performance of our method over the competing ones in detecting overlapping communities for all analysed data sets. Improved performance is particularly pronounced in cases of more complicated networked community structures
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