Gravity-Inspired Graph Autoencoders for Directed Link Prediction

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines.Comment: ACM International Conference on Information and Knowledge Management (CIKM 2019

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

A fast algorithm for predicting links to nodes of interest

The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we propose a fast similarity-based method to predict links related to nodes of interest. In the method, we first construct a sub-graph centered at the node of interest. By choosing the proper size for such a sub-graph, we can restrict the error of the estimated similarities within a given threshold. Because the similarity score is computed within a small sub-graph, the algorithm can greatly reduce computation time. The method is also extended to predict potential links in the whole network to achieve high process speed and accuracy. Experimental results on real networks demonstrate that our algorithm can obtain high accuracy results in less time than other methods can