Browsing by Author "Coşkun, Mustafa"

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Consensus embedding for multiple networks: Computation and applications
(CAMBRIDGE UNIV PRESS32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473, 2022) Li, Mengzhen; Coskun, Mustafa; Koyuturk, Mehmet; 0000-0002-2266-4313; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Coşkun, Mustafa
Machine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map the nodes into a lowdimensional space such that the nodes that are “similar” with respect to network topology are also close to each other in the embedding space. Real-world networks often have multiple versions or can be “multiplex” with multiple types of edges with different semantics. For such networks, computation of Consensus Embeddings based on the node embeddings of individual versions can be useful for various reasons, including privacy, efficiency, and effectiveness of analyses. Here, we systematically investigate the performance of three dimensionality reduction methods in computing consensus embeddings on networks with multiple versions: singular value decomposition, variational auto-encoders, and canonical correlation analysis (CCA). Our results show that (i) CCA outperforms other dimensionality reduction methods in computing concensus embeddings, (ii) in the context of link prediction, consensus embeddings can be used to make predictions with accuracy close to that provided by embeddings of integrated networks, and (iii) consensus embeddings can be used to improve the efficiency of combinatorial link prediction queries on multiple networks by multiple orders of magnitude
A high order proximity measure for linear network embedding
(Niğde Ömer Halisdemir Üniversitesi, 2022) Coşkun, Mustafa; 0000-0003-4805-1416; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Coşkun, Mustafa
Graph representationion learning (network embedding) is at the heart of network analytics techniques to reveal and examine the complex dependencies among nodes. Owing its importance, many computational methods have been proposed to solve a large volume of learning tasks on graphs, such as node classification, link prediction and clustering. Among various network embedding techniques, linear Matrix Factorization-based (MF) network embedding approaches have demonstrated to be very effective and efficient as they can be stated as singular value decomposition (SVD) problem, which can be efficiently solved by off-the-shelf eigen-solvers, such as Lanczos method. Despite the effectiveness of these linear methods, they rely on high order proximity measures, i.e., random walk restarts (RWR) and/or Katz, which have their own limitations, such as degree biasness, hyper-parameter dependency. In this paper, to alleviate the RWR and Katz depended high proximity usage in the linear embedding methods, we propose an algorithm that uses label propagation and shift-and-invert approach to resort RWR and Katz related problems. Testing our methods on realnetworks for link prediction task, we show that our algorithm drastically improves link prediction performance of network embedding comparing against an embedding approach that uses RWR and Katz high order proximity measures.
Intelligent traffic light systems using edge flow predictions
(ELSEVIER, 2024) Thahir, Adam Rizvi; Coşkun, Mustafa; Kılıç, Sultan Kübra; Gungor, Vehbi Cagrı; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Thahir, Adam Rizvi; Kılıç, Sultan Kübra; Gungor, Vehbi Cagrı
In this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations.