Anonymizing Social Graphs via Uncertainty Semantics

Rather than anonymizing social graphs by generalizing them to super nodes/edges or adding/removing nodes and edges to satisfy given privacy parameters, recent methods exploit the semantics of uncertain graphs to achieve privacy protection of participating entities and their relationship. These techniques anonymize a deterministic graph by converting it into an uncertain form. In this paper, we propose a generalized obfuscation model based on uncertain adjacency matrices that keep expected node degrees equal to those in the unanonymized graph. We analyze two recently proposed schemes and show their fitting into the model. We also point out disadvantages in each method and present several elegant techniques to fill the gap between them. Finally, to support fair comparisons, we develop a new tradeoff quantifying framework by leveraging the concept of incorrectness in location privacy research. Experiments on large social graphs demonstrate the effectiveness of our schemes

Efficient Subgraph Similarity Search on Large Probabilistic Graph Databases

Many studies have been conducted on seeking the efficient solution for subgraph similarity search over certain (deterministic) graphs due to its wide application in many fields, including bioinformatics, social network analysis, and Resource Description Framework (RDF) data management. All these works assume that the underlying data are certain. However, in reality, graphs are often noisy and uncertain due to various factors, such as errors in data extraction, inconsistencies in data integration, and privacy preserving purposes. Therefore, in this paper, we study subgraph similarity search on large probabilistic graph databases. Different from previous works assuming that edges in an uncertain graph are independent of each other, we study the uncertain graphs where edges' occurrences are correlated. We formally prove that subgraph similarity search over probabilistic graphs is #P-complete, thus, we employ a filter-and-verify framework to speed up the search. In the filtering phase,we develop tight lower and upper bounds of subgraph similarity probability based on a probabilistic matrix index, PMI. PMI is composed of discriminative subgraph features associated with tight lower and upper bounds of subgraph isomorphism probability. Based on PMI, we can sort out a large number of probabilistic graphs and maximize the pruning capability. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. The efficiency of our proposed solutions has been verified through extensive experiments.Comment: VLDB201

Mining Maximal Cliques from an Uncertain Graph

We consider mining dense substructures (maximal cliques) from an uncertain graph, which is a probability distribution on a set of deterministic graphs. For parameter 0 < {\alpha} < 1, we present a precise definition of an {\alpha}-maximal clique in an uncertain graph. We present matching upper and lower bounds on the number of {\alpha}-maximal cliques possible within an uncertain graph. We present an algorithm to enumerate {\alpha}-maximal cliques in an uncertain graph whose worst-case runtime is near-optimal, and an experimental evaluation showing the practical utility of the algorithm.Comment: ICDE 201

FREQUENT PATTERN MINING ON UNCERTAIN GRAPHS

Weakness is typical for a wide extent of affirmed applications, which unavoidably applies to diagram information. Specialist flawed charts are seen in bio-informatics, social affiliations, and so forth This paper rouses the issue of ordinary sub framework mining on single sketchy outlines, and researches two exceptional - probabilistic and expected - semantics to the degree help definitions. Diagram information are poor upon shortcomings in different applications because of inadequacy and imprecision of information. Mining unsure diagram information is semantically not actually identical to and computationally more testing than mining precise chart information. This paper assesses the issue of mining reformist sub chart plans from defective outline information. The reformist sub chart arrangement mining issue is formalized by masterminding another measure called anticipated assistance. A normal mining tally is proposed to discover a concluded arrangement of standard sub diagram designs by permitting a blunder strength on the regular sponsorships of the found sub chart plans. The check utilizes a beneficial measure calculation to pick if a sub diagram model can be yield or not. The savvy and exploratory outcomes show that the assessment is useful, precise and adaptable for enormous crude chart information bases

The Advantage of Evidential Attributes in Social Networks

Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes gives better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes.Comment: 20th International Conference on Information Fusion, Jul 2017, Xi'an, Chin

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page

A Maximum Variance Approach for Graph Anonymization

Best Paper AwardInternational audienceUncertain graphs, a form of uncertain data, have recently attracted a lot of attention as they can represent inherent uncertainty in collected data. The uncertain graphs pose challenges to conventional data processing techniques and open new research directions. Going in the reserve direction, this paper focuses on the problem of anonymizing a deterministic graph by converting it into an uncertain form. The paper first analyzes drawbacks in a recent uncertainty-based anonymization scheme and then proposes Maximum Variance, a novel approach that provides better tradeoff between privacy and utility. Towards a fair com-parison between the anonymization schemes on graphs, the second con-tribution of this paper is to describe a quantifying framework for graph anonymization by assessing privacy and utility scores of typical schemes in a unified space. The extensive experiments show the effectiveness and efficiency of Maximum Variance on three large real graphs