Sadegh Nobari

Fast random graph generation

Today, several database applications call for the generation of random graphs. A fundamental, versatile random graph model adopted for that purpose is the Erdős-Rényi Γv,p model. This model can be used for directed, undirected, and multipartite graphs, with and without self-loops; it induces algorithms for both graph generation and sampling, hence is useful not only in applications necessitating the generation of random structures but also for simulation, sampling and in randomized algorithms. However, the commonly advocated algorithm for random graph generation under this model performs poorly when generating large graphs, and fails to make use of the parallel processing capabilities of modern hardware. In this paper, we propose PPreZER, an alternative, data parallel algorithm for random graph generation under the Erdős-Rényi model, designed and implemented in a graphics processing unit (GPU). We are led to this chief contribution of ours via a succession of seven intermediary algorithms, both sequential and parallel. Our extensive experimental study shows an average speedup of 19 for PPreZER with respect to the baseline algorithm.

TOUCH: in-memory spatial join by hierarchical data-oriented partitioning

Efficient spatial joins are pivotal for many applications and particularly important for geographical information systems or for the simulation sciences where scientists work with spatial models. Past research has primarily focused on disk-based spatial joins; efficient in-memory approaches, however, are important for two reasons: a) main memory has grown so large that many datasets fit in it and b) the in-memory join is a very time-consuming part of all disk-based spatial joins. In this paper we develop TOUCH, a novel in-memory spatial join algorithm that uses hierarchical data-oriented space partitioning, thereby keeping both its memory footprint and the number of comparisons low. Our results show that TOUCH outperforms known in-memory spatial-join algorithms as well as in-memory implementations of disk-based join approaches. In particular, it has a one order of magnitude advantage over the memory-demanding state of the art in terms of number of comparisons (i.e., pairwise object comparisons), as well as execution time, while it is two orders of magnitude faster when compared to approaches with a similar memory footprint. Furthermore, TOUCH is more scalable than competing approaches as data density grows.

Scalable parallel minimum spanning forest computation

The proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graphs minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper we propose a novel, scalable, parallel MSF algorithm for undirected weighted graphs. Our algorithm leverages Prims algorithm in a parallel fashion, concurrently expanding several subsets of the computed MSF. Our effort focuses on minimizing the communication among different processors without constraining the local growth of a processors computed subtree. In effect, we achieve a scalability that previous approaches lacked. We implement our algorithm in CUDA, running on a GPU and study its performance using real and synthetic, sparse as well as dense, structured and unstructured graph data. Our experimental study demonstrates that our algorithm outperforms the previous state-of-the-art GPU-based MSF algorithm, while being several orders of magnitude faster than sequential CPU-based algorithms.

L-opacity: Linkage-aware graph anonymization

The wealth of information contained in online social networks has created a demand for the publication of such data as graphs. Yet, publication, even after identities have been removed, poses a privacy threat. Past research has suggested ways to publish graph data in a way that prevents the re-identification of nodes. However, even when identities are effectively hidden, an adversary may still be able to infer linkage between individuals with sufficiently high confidence. In this paper, we focus on the privacy threat arising from such link disclosure. We suggestL-opacity, a sufficiently strong privacy model that aims to control an adversary’s confidence on short multiedge linkages among nodes. We propose an algorithm with two variant heuristics, featuring a sophisticated look-ahead mechanism, which achieves the desired privacy guarantee after a few graph modifications. We empirically evaluate the performance of our algorithm, measuring the alteration inflicted on graphs and various utility metrics quantifying spectral and structural graph properties, while we also compare them to a recently proposed, albeit limited in generality of scope, alternative. Thereby, we demonstrate that our algorithms are more general, effective, and efficient than the competing technique, while our heuristic that preserves the number of edges in the graph constant fares better overall than one that reduces it.

Discretionary social network data revelation with a user-centric utility guarantee

The proliferation of online social networks has created intense interest in studying their nature and revealing information of interest to the end user. At the same time, such revelation raises privacy concerns. Existing research addresses this problem following an approach popular in the database community: a model of data privacy is defined, and the data is rendered in a form that satisfies the constraints of that model while aiming to maximize some utility measure. Still, these is no consensus on a clear and quantifiable utility measure over graph data. In this paper, we take a different approach: we define a utility guarantee, in terms of certain graph properties being preserved, that should be respected when releasing data, while otherwise distorting the graph to an extend desired for the sake of confidentiality. We propose a form of data release which builds on current practice in social network platforms: A user may want to see a subgraph of the network graph, in which that user as well as connections and affiliates participate. Such a snapshot should not allow malicious users to gain private information, yet provide useful information for benevolent users. We propose a mechanism to prepare data for user view under this setting. In an experimental study with real data, we demonstrate that our method preserves several properties of interest more successfully than methods that randomly distort the graph to an equal extent, while withstanding structural attacks proposed in the literature.

Computational Neuroscience Breakthroughs through Innovative Data Management

Simulations have become key in many scientific disciplines to better understand natural phenomena. Neuroscientists, for example, build and simulate increasingly fine-grained models including subcellular details, e.g., neurotransmitter of the neocortex to understand the mechanisms causing brain diseases and to test new treatments in-silico. The sheer size and, more importantly, the level of detail of their models challenges todays spatial data management techniques. In collaboration with the Blue Brain project BBP we develop new approaches that efficiently enable analysis, navigation and discovery in spatial models of the brain. More precisely, we develop an index for the scalable and efficient execution of spatial range queries supporting model building and analysis. Furthermore, we enable navigational access to the brain models, i.e., the execution of of series of range queries where he location of each query depends on the previous ones. To efficiently support navigational access, we develop a method that uses previous query results to prefetch spatial data with high accuracy and therefore speeds up navigation. Finally, to enable discovery based on the range queries, we conceive a novel in-memory spatial join. The methods we develop considerably outperform the state of the art, but more importantly, they enable the neuroscientists to scale to building, simulating and analyzing massively bigger and more detailed brain models.

On the Privacy and Utility of Anonymized Social Networks

One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.

Edit distance between XML and probabilistic XML documents

Probabilistic XML is a hierarchical data model capturing uncertainty of both value and structure. The ability to compute the similarity between an XML document and a probabilistic XML document is a building block of many applications involving querying, comparison, alignment and classification, for instance. The new challenge in efficiently computing such similarity is the multiplicity of the possible worlds represented by a probabilistic XML document. We devise and discuss an algorithm for the efficient computation of the similarity between an XML document and a probabilistic XML document. We empirically and comparatively evaluate the performance of the algorithm and its variants.