Dense subgraph detection is a fundamental building block for a va- riety of applications. Most of the existing methods aim to discover dense subgraphs within either a single network or a multi-view network while ignoring the informative node dependencies across multiple layers of networks in a complex system. To date, it largely remains a daunting task to detect dense subgraphs on multi-layered networks. In this paper, we formulate the problem of dense sub- graph detection on multi-layered networks based on cross-layer consistency principle. We further propose a novel algorithm Des- tine based on projected gradient descent with the following ad- vantages. First, armed with the cross-layer dependencies, Destine is able to detect significantly more accurate and meaningful dense subgraphs at each layer. Second, it scales linearly w.r.t. the num- ber of links in the multi-layered network. Extensive experiments demonstrate the efficacy of the proposed Destine algorithm in various cases.
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Dissecting Cross-Layer Dependency Inference on Multi-Layered Inter-Dependent Networks
Multi-layered inter-dependent networks have emerged in a wealth of high-impact application domains. Cross-layer dependency inference, which aims to predict the dependencies between nodes across different layers, plays a pivotal role in such multi-layered network systems. Most, if not all, of existing methods exclusively follow a coupling principle of design and can be categorized into the following two groups, including (1) heterogeneous network embedding based methods (data coupling), and (2) collaborative filtering based methods (module coupling). Despite the favorable achievement, methods of both types are faced with two intricate challenges, including (1) the sparsity challenge where very limited observations of cross-layer dependencies are available, resulting in a deteriorated prediction of missing dependencies, and (2) the dynamic challenge given that the multi-layered network system is constantly evolving over time.
In this paper, we first demonstrate that the inability of existing methods to resolve the sparsity challenge roots in the coupling principle from the perspectives of both data coupling and module coupling. Armed with such theoretical analysis, we pursue a new principle where the key idea is to decouple the within-layer connectivity from the observed cross-layer dependencies. Specifically, to tackle the sparsity challenge for static networks, we propose FITO-S, which incorporates a position embedding matrix generated by random walk with restart and the embedding space transformation function. More essentially, the decoupling principle ameliorates the dynamic challenge, which naturally leads to FITO-D, being capable of tracking the inference results in the dynamic setting through incrementally updating the position embedding matrix and fine-tuning the space transformation function. Extensive evaluations on real-world datasets demonstrate the superiority of the proposed framework FITO for cross-layer dependency inference.
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- NSF-PAR ID:
- 10380768
- Date Published:
- Journal Name:
- CIKM
- Page Range / eLocation ID:
- 2341 to 2351
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Motivation Protein function prediction, based on the patterns of connection in a protein–protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low-dimensional embeddings that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein–protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in protein–protein association networks, capturing implicit local and global (i.e. embedding-based) graph properties.
Results GLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four human protein–protein association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein–protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein–protein association networks. Furthermore, we introduce the GLIDER graph neighborhood as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson’s Disease GWAS genes, rediscover many genes which have known involvement in Parkinson’s disease pathways, plus suggest some new genes to study.
Availability and implementation All code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER.
Supplementary information Supplementary data are available at Bioinformatics online.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3511808.3557291
