Browsing by Author "Aksu, Hidayet"

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Open Access
An analysis of social networks based on tera-scale telecommunication datasets
(IEEE Computer Society, 2019) Aksu, Hidayet; Körpeoğlu, İbrahim; Ulusoy, Özgür
With the popularization of mobile phone usage, telecommunication networks have turned into a socially binding medium. Considering the traces of human communication held inside these networks, telecommunication networks are now able to provide a proxy for human social networks. To study degree characteristics and structural properties in large-scale social networks, we gathered a tera-scale dataset of call detail records that contains ≈ 5 × 10 7 nodes and ≈ 3.6 × 10 10 links for three GSM (mobile) networks, as well as ≈ 1.4 × 10 7 nodes and ≈ 1.9 × 10 9 links for one PSTN (fixed-line) network. In this paper, we first empirically evaluate some statistical models against the degree distribution of the country's call graph and determine that a Pareto log-normal distribution provides the best fit, despite claims in the literature that power-law distribution is the best model. We then question how network operator, size, density, and location affect degree distribution to understand the parameters governing it in social networks. Our empirical analysis indicates that changes in density, operator and location do not show a particular correlation with degree distribution; however, the average degree of social networks is proportional to the logarithm of network size. We also report on the structural properties of the communication network. These novel results are useful for managing and planning communication networks.
Open Access
Efficient analysis of large-scale social networks using big-data platforms
(2014) Aksu, Hidayet
In recent years, the rise of very large, rich content networks re-ignited interest to complex/social network analysis at the big data scale, which makes it possible to understand social interactions at large scale while it poses computation challenges to early works with algorithm complexity greater than O(n). This thesis analyzes social networks at very large-scales to derive important parameters and characteristics in an efficient and effective way using big-data platforms. With the popularization of mobile phone usage, telecommunication networks have turned into a socially binding medium and enables researches to analyze social interactions at very large scales. Degree distribution is one of the most important characteristics of social networks and to study degree characteristics and structural properties in large-scale social networks, in this thesis we first gathered a tera-scale dataset of telecommunication call detail records. Using this data we empirically evaluate some statistical models against the degree distribution of the country’s call graph and determine that a Pareto log-normal distribution provides the best fit, despite claims in the literature that power-law distribution is the best model. We also question and derive answers for how network operator, size, density and location affect degree distribution to understand the parameters governing it in social networks. Besides structural property analysis, community identification is of great interest in practice to learn high cohesive subnetworks about different subjects in a social network. In graph theory, k-core is a key metric used to identify subgraphs of high cohesion, also known as the ‘dense’ regions of a graph. As the real world graphs such as social network graphs grow in size, the contents get richer and the topologies change dynamically, we are challenged not only to materialize k-core subgraphs for one time but also to maintain them in order to keep up with continuous updates. These challenges inspired us to propose a new set of distributed algorithms for k-core view construction and maintenance on a horizontally scaling storage and computing platform. Experimental evaluation results demonstrated orders of magnitude speedup and advantages of maintaining k-core incrementally and in batch windows over complete reconstruction approaches. Moreover, the intensity of community engagement can be distinguished at multiple levels, resulting in a multiresolution community representation that has to be maintained over time. We also propose distributed algorithms to construct and maintain a multi-k-core graphs, implemented on the scalable big-data platform Apache HBase. Our experimental evaluation results demonstrate orders of magnitude speedup by maintaining multi-k-core incrementally over complete reconstruction. Furthermore, we propose a graph aware cache system designed for distributed graph processing. Experimental results demonstrate up to 15x speedup compared to traditional LRU based cache systems.
Open Access
Graph aware caching policy for distributed graph stores
(IEEE, 2015-03) Aksu, Hidayet; Canım, M.; Chang, Y.-C.; Körpeoğlu, İbrahim; Ulusoy, Özgür
Graph stores are becoming increasingly popular among NOSQL applications seeking flexibility and heterogeneity in managing linked data. Conceptually and in practice, applications ranging from social networks, knowledge representations to Internet of things benefit from graph data stores built on a combination of relational and non-relational technologies aimed at desired performance characteristics. The most common data access pattern in querying graph stores is to traverse from a node to its neighboring nodes. This paper studies the impact of such traversal pattern to common data caching policies in a partitioned data environment where a big graph is distributed across servers in a cluster. We propose and evaluate a new graph aware caching policy designed to keep and evict nodes, edges and their metadata optimized for query traversal pattern. The algorithm distinguishes the topology of the graph as well as the latency of access to the graph nodes and neighbors. We implemented graph aware caching on a distributed data store Apache HBase in the Hadoop family. Performance evaluations showed up to 15x speedup on the benchmark datasets preferring our new graph aware policy over non-aware policies. We also show how to improve the performance of existing caching algorithms for distributed graphs by exploiting the topology information. © 2015 IEEE.
Open Access
An inquiry into the metrics for evaluation of localization algorithms in wireless ad hoc and sensor networks
(2008) Aksu, Hidayet
In ad-hoc and sensor networks, the location of a sensor node making an observation is a vital piece of information to allow accurate data analysis. GPS is an established technology to enable precise position information. Yet, resource constraints and size issues prohibit its use in small sensor nodes that are designed to be cost efficient. Instead, most positions are estimated by a number of algorithms. Such estimates, inevitably introduce errors in the information collected from the field, and it is very important to determine the error in cases where they lead to inaccurate data analysis. After all, many components of the application rely on the reported locations including decision making processes. It is, therefore, vital to understand the impact of errors from the applications’ point of view. To date, the focus on location estimation was on individual accuracy of each sensor’s position in isolation to the complete network. In this thesis, we point out the problems with such an approach that does not consider the complete network topology and the relative positions of nodes in comparison to each other. We then describe the existing metrics, which are used in the literature, and also propose some novel metrics that can be used in this area of research. Furthermore, we run simulations to understand the behavior of the existing and proposed metrics. After having discussed the simulation results, we suggest a metric selection methodology that can be used for wireless sensor network applications.
Open Access
Multi-resolution social network community identification and maintenance on big data platform
(IEEE, 2013-06-07) Aksu, Hidayet; Canım, M.; Chang, Y.-C.; Körpeoğlu, İbrahim; Ulusoy, Özgür
Community identification in social networks is of great interest and with dynamic changes to its graph representation and content, the incremental maintenance of community poses significant challenges in computation. Moreover, the intensity of community engagement can be distinguished at multiple levels, resulting in a multi-resolution community representation that has to be maintained over time. In this paper, we first formalize this problem using the k-core metric projected at multiple k values, so that multiple community resolutions are represented with multiple k-core graphs. We then present distributed algorithms to construct and maintain a multi-k-core graph, implemented on the scalable big-data platform Apache HBase. Our experimental evaluation results demonstrate orders of magnitude speedup by maintaining multi-k-core incrementally over complete reconstruction. Our algorithms thus enable practitioners to create and maintain communities at multiple resolutions on different topics in rich social network content simultaneously. © 2013 IEEE.