Link Prediction in a Weighted Network Using Support Vector Machine

Link prediction is a field under network analysis that deals with the existence or emergence of links. In this study, we investigate the effect of using weighted networks for two link prediction techniques, which are the Vector Auto Regression (VAR) technique and our proposed modified VAR that uses Support Vector Machine (SVM). Using a co-authorship network from DBLP as the dataset and the Area Under the Receiver Operating Curve (AUC-ROC) as the fitness metric, the results show that the performance of both VAR and SVM are surprisingly lower in the weighted network than in the unweighted network. In an attempt to improve the results in the weighted network, we incorporated features from the unweighted network into the features of the weighted network. This enhancement improved the performance of both VAR and SVM, but the results are still inferior to those in the unweighted networks. We identified that the true positive rate was generally lower in the weighted network, thus resulting to a lower AUC

Consensus Embedding for Multiple Networks: Computation and Applications

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 low-dimensional 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

Ağ topolojisi ilişkisi ile bağlantı tahmin yöntemlerinin performanslarının keşfi

One of the prominent topics in complex network analysis is link prediction, which is a key component of network-based recommendation systems or finding missing connections. There are several different link prediction methods in the literature based on measuring the likelihood of the existence of a link between two nodes. These methods use different topological properties of the network. Although there are methods using different strategies, previous studies have focused only on method success but have not adequately examined the relationship between the performance of these methods and the topology of the network. The main motivation for this study is to reveal the role of different network topologies in link prediction. Thus, the choice of link prediction method can be customized according to the topological characteristics of the network. The two main contributions of the study are, firstly, comparing different link prediction methods with well-known performance measures in social, biological, and information networks with different topological properties in a large experimental setup; and second, examining the possible relationship between the performance of link prediction methods and the network topology. Based on the experimental results, the global methods are more successful than others, regardless of the network topology. In addition, it was concluded that the high eigenvector centralization in the network may affect the missing link prediction performance.Karmaşık ağ analizinde öne çıkan konulardan biri, ağ tabanlı öneri sistemlerinin veya eksik bağlantıların bulunmasının önemli bir bileşeni olan bağlantı tahminidir. Literatürde iki düğüm arasında bağlantı bulunma şansını ölçümlemeye dayanan birçok farklı bağlantı tahmini yöntemi vardır. Bu yöntemler ağın farklı topolojik özelliklerini kullanır. Çok farklı stratejiler kullanan yöntemler bulunmasına rağmen, önceki çalışmalar yalnızca yöntem başarısına odaklanmış ama bu yöntemlerin performansının ağın topolojisi ile ilişkisini yeteri kadar incelememiştir. Bu çalışmanın ana motivasyonu farklı ağ topolojilerininin bağlantı tahminindeki rolünü bir ortaya koymaktır. Böylece ağın topolojik özelliklerine göre bağlantı tahmin yöntemi seçimi özelleştirilebilir. Çalışmanın iki temel katkısı, ilk olarak, büyük bir deney düzeneğinde farklı topolojik özelliklere sahip sosyal, biyolojik ve bilgi ağlarında iyi bilinen performans ölçümleriyle farklı bağlantı tahmin yöntemlerini karşılaştırmak ve ikincisi, bağlantı tahmin yöntemlerinin performansı ile ağ topolojisi arasındaki olası ilişkinin incelenmesi olarak sıralanabilir. Sonuçlara göre, ağ topolojisine bakılmaksızın küresel yöntemlerin diğerlerinden daha başarılı olduğunu gördük. Ayrıca, ağda özvektör merkezileşmesinin yüksek olmasının eksik bağlantı tahmin performansını etkileyebileceği sonucuna ulaşıldı