Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics

Abstract

Frequent subgraph mining has been extensively studied on certain graph data. However, uncertainties are inherently accompanied with graph data in practice, and there is very few work on mining uncertain graph data. This paper investigates frequent subgraph mining on uncertain graphs under probabilistic semantics. Specifically, a measure called ϕ-frequent probability is introduced to evaluate the degree of recurrence of subgraphs. Given a set of uncertain graphs and two numbers 0 < ϕ, τ < 1, the goal is to quickly find all subgraphs with ϕ-frequent probability at least τ . Due to the NP-hardness of the problem, an approximate mining algorithm is proposed for this problem. Let 0 < δ < 1 be a parameter. The algorithm guarantees to find any frequent subgraph S with probability at least 1−δ 2 s , where s is the number of edges of S. In addition, it is thoroughly discussed how to set δ to guarantee the overall approximation quality of the algorithm. The extensive experiments on real uncertain graph data verify that the algorithm is efficient and that the mining results have very high quality.

Distance-constraint reachability computation in uncertain graphs

“…Managing and mining uncertain graphs has recently attracted much attention in the database and data mining research community [13,23,24,25]. Especially, Potamias et.…”

“…Querying and mining uncertain graphs has become an increasingly important research topic [13,24,25]. In the most common uncertain graph model, edges are independent of one another, and each edge is associated with a probability that indicates the likelihood of its existence [13,24].…”

“…In the most common uncertain graph model, edges are independent of one another, and each edge is associated with a probability that indicates the likelihood of its existence [13,24]. This gives rise to using the possible world semantics to model uncertain graphs [13,1].…”

A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark

“…FSM on uncertain graph transactions under expected semantics considers a subgraph frequent if its expected support is greater than the threshold. Representative algorithms include Mining Uncertain Subgraph patterns (MUSE) [34], Weighted MUSE (WMUSE) [35], Uncertain Graph Index(UGRAP) [36] and Mining Uncertain Subgraph patterns under Probabilistic semantics (MUSE-P) [37]. They are proposed under expected semantics or the probabilistic semantics.…”

On Uncertain Probabilistic Data Modeling

“…Modeling, querying and mining uncertain graphs have become an increasingly important research topic [19][20][21] recently. Probabilistic graphs are a natural model representation in many applications, such as mobile ad-hoc networks, social networks, traffic networks, biological networks, genome databases, medical records, etc.…”