Mask Combination of Multi-Layer Graphs for Global Structure Inference

Date

2020-01-01

Author

Bayram, Eda
Thanou, Dorina
Vural, Elif
Frossard, Pascal

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

298
views

0
downloads

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a data space whose inner relationships are encoded by multi-layer graphs. We aim at properly exploiting the information originating from each layer to infer the global structure underlying the signals. We thus present a novel method for combining the multiple graphs into a global graph using mask matrices, which are estimated through an optimization problem that accommodates the multi-layer graph information and a signal representation model. The proposed mask combination method also estimates the contribution of each graph layer in the structure of signals. The experiments conducted both on synthetic and real-world data suggest that integrating the multi-layer graph representation of the data in the structure inference framework enhances the learning procedure considerably by adapting to the quality and the quantity of the input data.

Subject Keywords

Task analysis, Social network services, Semisupervised learning, Signal representation, Data analysis, Laplace equations, Information processing, Multi-relational networks, Multi-view data analysis, Network data analysis, Graph signal processing, Structure inference, Link prediction, Graph learning

URI

https://hdl.handle.net/11511/34642

Journal

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS

DOI

https://doi.org/10.1109/tsipn.2020.2995515

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Estimation of partially observed multiple graph signals by learning spectrally concentrated graph kernels Turhan, Gülce; Vural, Elif; Department of Electrical and Electronics Engineering (2021-3-31) Graph models provide flexible tools for the representation and analysis of signals defined over domains such as social or sensor networks. However, in real applications data observations are often not available over the whole graph, due to practical problems such as broken sensors, connection loss, or storage problems. In this thesis, we study the problem of estimating partially observed graph signals on multiple graphs. We consider possibly multiple graph domains over which a set of signals is available wi...
Using data analytics for collaboration patterns in distributed software team simulations Dafoulas, Georgios A.; Serce, Fatma C.; SWİGGER, Kathleen; BRAZİLE, Robert; Alpaslan, Ferda Nur; Alpaslan, Ferda Nur; Milewski, Allen (2016-08-05) This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of variou...
ESTIMATION OF TIME VARYING GRAPH SIGNALS WITH GRAPH ARMA PROCESSES Güneyi, Eylem Tuğçe; Vural, Elif; Department of Electrical and Electronics Engineering (2021-9-8) Graph models provide efficient tools for analyzing data defined over irregular domains such as social networks, sensor networks, and transportation networks. Real-world graph signals are usually time-varying signals. The characterization of the joint behavior of time-varying graph signals in the time and the vertex domains has recently arisen as an interesting research problem, contrasted to the independent processing of graph signals acquired at different time instants. The concept of wide sense stationari...
Activity Learning from Lifelogging Images Belli, Kader; Akbaş, Emre; Yazıcı, Adnan (2019-01-01) The analytics of lifelogging has generated great interest for data scientists because big and multi-dimensional data are generated as a result of lifelogging activities. In this paper, the NTCIR Lifelog dataset is used to learn activities from an image point of view. Minute definitions are classified into activity classes using images and annotations, which serve as a basis for various classification techniques, namely SVMs and convolutional neural network structures (CNN), for learning activities. The perf...
IMAGE-BASED OCCUPANCY SENSING AND PRIVACY IMPLICATIONS Haroon, Hammad; Pekeriçli, Mehmet Koray; Department of Building Science in Architecture (2022-7-07) As the use of data collection in the built environment increased, data pertaining to building occupancy has gained considerable importance in realms such as energy optimization and spatial usage analytics. However, many data collection approaches infringe on individuals’ rights to privacy, and subsequently their comfort. This thesis aims to address the tension between the proliferation of smart building technologies and individual privacy and autonomy, specifically focusing on image-based sensing. It explor...

Citation Formats

E. Bayram, D. Thanou, E. Vural, and P. Frossard, “Mask Combination of Multi-Layer Graphs for Global Structure Inference,” IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, pp. 394–406, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34642.