Academic Positions

Education & Training

  • Ph.D. -

    Ph.D. in Computer Science

    Georgia Tech. College of Computing

  • M.S..2015

    Master of Science in Computer Engineering

    Bilkent University

  • B.S.2013

    Bachelor of Science in Computer Engineering

    Université Galatasaray

Knowledge without research eventually stagnates! *

Honors, Awards and Grants

  • 2018
    Selected as one of the HPEC 2018 Graph Challenge Champions
  • 2015
    Graduate Research Assistanship - Georgia Tech.
  • 2013
    Graduate Research Assistanship - Bilkent University
  • 2012
    Excellence study grant provided by the Embassy of France in Turkey
  • 2012
    First Grade, Université Galatasaray
  • 2012
    Special Jury Award, Team ONGUN, IBM Software Academy, Turkey
  • 2011
    French Institute for Research in Computer Science and Automation Fellowship for summer internship
  • 2008
    Galatasaray Education Foundation (GEV) Bachelors Degree Fellowship.

Research Summary

I conduct research in the general area of data-intensive distributed systems, with a particular focus on fast data and big data. My research interests include scalable graph analytics, distributed and single node graph processing middlewares, MapReduce systems. My research addresses issues such as parallelization, fault-tolerance, profiling and optimization, and scalable data mining, especially in the context of social graphs.

Currently I am a member of Prof. Umit Catalyurek's HPC Lab as a PhD. student.

My dissertation research focused on a scalable Map/Reduce-style method called ICBP, which can divide the graph into a series of disk blocks that contain sub-graphs with high locality.


  • Linear Algebra based Scalable Graph Algorithms
  • Graph Alignment
  • Scalable Graph Analytics
  • Graph Processing Middlewares
  • Parallel and Distributed Algorithms
  • Big Data

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Fast Triangle Counting Using Cilk (one of the Graph Challenge Champions)

Abdurrahman Yaşar, Sivasankaran Rajamanickam, Michael M. Wolf, Jonathan. W. Berry, Ümit V. Çatalyürek
Conference Papers HPEC 2018


Triangle counting is a representative graph analysis algorithm with several applications. It is also one of the three benchmarks used in the IEEE HPEC Graph Challenge. Triangle counting can be expressed as a graph algorithm and in a linear algebra formulation using sparse matrix-matrix multiplication (SpGEMM). The linear algebra formulation using the SpGEMM algorithm in the Kokkoskernels library was one of the fastest implementations for the triangle counting problem in last year’s Graph Challenge. This paper improves upon that work by developing an SpGEMM implementation that relies on a highly efficient, work-stealing, multithreaded runtime. We demonstrate that this new implementation results in improving the triangle counting runtime up to 5x to 12x on different architectures. This new implementation also breaks the 109 barrier for the rate measure on a single node for the triangle counting problem. We also compare the linear algebra formulation with a traditional graph based formulation. The linear algebra implementation is up to 2:96x to 7x faster on different architectures compared to the graph based implementation. Furthermore, we present analysis of the scaling of the triangle counting implementation as the graph sizes increase using both synthetic and real graphs from the graph challenge data set.

Programming strategies for irregular algorithms on the Emu Chick

E. Hein, S. Eswar, Abdurrahman Yasar, B. Uçar, Ü. Çatalyürek, T. Conte, J. Riedy, R. Vuduc, and J. S. Young
Conference Papers Submitted


submitted. Under Review.

An Iterative Global Structure-Assisted Labeled Network Aligner

Abdurrahman Yasar, Umit Catalyurek
Conference Papers KDD 2018


Integrating data from heterogeneous sources is often modeled as merging graphs. Given two or more 'compatible', but not-isomorphic graphs, the first step is to identify a graph alignment, where a potentially partial mapping of vertices between two graphs is computed. A significant portion of the literature on this problem only takes the global structure of the input graphs into account. Only more recent ones additionally use vertex and edge attributes to achieve a more accurate alignment. However, these methods are not designed to scale to map large graphs arising in many modern applications. We propose a new iterative graph aligner, gsaNA, that uses the global structure of the graphs to significantly reduce the problem size and align large graphs with a minimal loss of information. Concretely, we show that our proposed technique is highly flexible, can be used to achieve higher recall, and it is orders of magnitudes faster than the current state of the art techniques.

SINA: A Scalable Iterative Network Aligner

Abdurrahman Yasar, Bora Ucar, Umit Catalyurek
Conference Papers ASONAM 2018


Given two graphs, network alignment asks for a potentially partial mapping between the vertices of the two graphs. This arises in many applications where data from different sources need to be integrated. Recent graph aligners use the global structure of input graphs and additional information given for the edges and vertices. We present SINA, an efficient, shared memory parallel implementation of such an aligner. Our experimental evaluations on a 32-core shared memory machine showed that SINA scales well for aligning large real-world graphs: SINA can achieve up to 28: speedup, and can reduce the total execution time of a graph alignment problem with 2M vertices and 100M edges from 4.5 hours to under 10 minutes. To the best of our knowledge, SINA is the first parallel aligner that uses global structure and vertex and edge attributes to handle large graphs.

Distributed block formation and layout for disk-based management of large-scale graphs

Abdurrahman Yasar, Bugra Gedik, Hakan Ferhatosmanoglu
Journal Papers Journal of Distributed and Parallel Databases


We are witnessing an enormous growth in social networks as well as in the volume of data generated by them. An important portion of this data is in the form of graphs. In recent years, several graph processing and management systems emerged to handle large-scale graphs. The primary goal of these systems is to run graph algorithms and queries in an efficient and scalable manner. Unlike relational data, graphs are semi-structured in nature. Thus, storing and accessing graph data using secondary storage requires new solutions that can provide locality of access for graph processing workloads. In this work, we propose a scalable block formation and layout technique for graphs, which aims at reducing the I/O cost of disk- based graph processing algorithms. To achieve this, we designed a scalable MapReduce-style method called ICBL, which can divide the graph into a series of disk blocks that contain sub-graphs with high locality. Furthermore, ICBL can order the resulting blocks on disk to further reduce non-local accesses. We experimentally evaluated ICBL to showcase its scalability, layout quality, as well as the effectiveness of automatic parameter tuning for ICBL. We deployed the graph layouts generated by ICBL on the Neo4j open source graph database, (2015) graph database management system. Our results show that the layout generated by ICBL reduces the query running times over Neo4j more than compared to the default layout.

Distributing Data by Successive Spatial Partitionings

Patent US Patent (filed)

Machine to Machine Trust in the IoT Era

L Liu, M Loper, Y Ozkaya, A Yasar, E Yigitoglu
Conference Papers AAMAS 2016 Proceedings of the 18th International Workshop on Trust in Agent Societies co-located with the 15th International Conference on Autonomous Agents and Multiagent Systems


Machine to machine communications are at the center stage of the Internet of things (IoT). Connecting the physical world with the digital world not only creates new opportunities for innovation and discovery, but also opens doors for misuse and abuse. This paper argues that reputation based trust can be an effective countermeasure for securing machine-to-machine communications. We propose to establish machine-to-machine trust by taking into account both transaction/interaction service behaviors and feedback rating behaviors in the presence of bogus transactions and dishonest feedbacks. Our machine-to-machine trust model, called M2MTrust, introduces two novel trust metrics: (1) pairwise similarity based feedback credibility and (2) threshold-controlled trust propagation. We compute the direct trust from machine A to machine B by utilizing their pairwise rating similarity as the weight to the normalized aggregate of ratings that A has given to B. Our direct trust computation model can effectively constrain malicious nodes to gain direct trusts from dishonest feedback ratings by leveraging feedback credibility. Furthermore, our threshold-controlled trust propagation mechanism can successfully block the trust propagation from good nodes to malicious nodes. We conduct extensive experiments using simulation and real datasets and the experimental results show that M2MTrust significantly outperforms other trust metrics in terms of both attack resilience and performance in the presence of dishonest feedbacks and sparse feedback ratings against four representative attack models.

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    Graph Alignment

    In this project, we study the problem of using global structure of the graphs to decrease the problem size and align larger graphs with a minimal loss of information.

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    Our goal was to propose a Computation oriented and I/O E cient graph processing system. In Stallion we have propesed a novel layout for graphs on disk and also a new programming API.

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    In this work, we propose a scalable block formation and layout technique for graphs, which aims at reducing the I/O cost of disk-based graph processing algorithms

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    Objective of Rh++ was to develop a social responsibility project whose goal is to accelarate and optimize pre-transfusion procedures of Turkish hospitals. Rh++ aims to nd and redirect donors to centers quickly in need. For this we built a distributed system and necessary applications for users, donors and centers.

At My Office

My office is located at Klaus Advanced Computing Building 1211 High Performance Computing Lab. In general I am at my office during the weekdays, however before coming sending me an email for appointment should be better.

Contact & Meet Me