9,924 research outputs found
Outlier Detection from Network Data with Subnetwork Interpretation
Detecting a small number of outliers from a set of data observations is
always challenging. This problem is more difficult in the setting of multiple
network samples, where computing the anomalous degree of a network sample is
generally not sufficient. In fact, explaining why the network is exceptional,
expressed in the form of subnetwork, is also equally important. In this paper,
we develop a novel algorithm to address these two key problems. We treat each
network sample as a potential outlier and identify subnetworks that mostly
discriminate it from nearby regular samples. The algorithm is developed in the
framework of network regression combined with the constraints on both network
topology and L1-norm shrinkage to perform subnetwork discovery. Our method thus
goes beyond subspace/subgraph discovery and we show that it converges to a
global optimum. Evaluation on various real-world network datasets demonstrates
that our algorithm not only outperforms baselines in both network and high
dimensional setting, but also discovers highly relevant and interpretable local
subnetworks, further enhancing our understanding of anomalous networks
Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops
This paper presents an approach for identifying the root causes of collective
anomalies given observational time series and an acyclic summary causal graph
which depicts an abstraction of causal relations present in a dynamic system at
its normal regime. The paper first shows how the problem of root cause
identification can be divided into many independent subproblems by grouping
related anomalies using d-separation. Further, it shows how, under this
setting, some root causes can be found directly from the graph and from the
time of appearance of anomalies. Finally, it shows, how the rest of the root
causes can be found by comparing direct effects in the normal and in the
anomalous regime. To this end, an adjustment set for identifying direct effects
is introduced. Extensive experiments conducted on both simulated and real-world
datasets demonstrate the effectiveness of the proposed method.Comment: Proceedings of the 26th International Conference on Artificial
Intelligence and Statistics (AISTATS) 2023, Valencia, Spai
- …