Reliability Maximization in Uncertain Graphs

Ke, Xiangyu; Khan, Arijit; Hasan, Mohammad Al; Rezvansangsari, Rojin

doi:10.1109/tkde.2020.2987570

Cited by 5 publications

(4 citation statements)

References 46 publications

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“…Uncertain graphs are undirected graphs with uncertainties represented as

= ( V , E , p ) ( Khan et al, 2018b ; Ke et al, 2020 ). Of these, V = { v 1 , v 2 , …, v n } refers to the node set, E ⊆ V × V refers to the probabilistic edge set, and p : E →(0, 1] is a function denoting the likelihood of the existence of each edge in E .…”

Section: Methodsmentioning

confidence: 99%

“…The probability that a certain graph G ∈ W (

) is implied from an uncertain graph

, which is defined by Eq. 1 ( Khan et al, 2018b ; Ke et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…Considering above problems, the concept of the uncertain network was introduced to characterize the uncertainty of functional connections ( Kong et al, 2013 ; Cao et al, 2015a , b ; Saha et al, 2021 ). Uncertain networks are based on uncertain graph theory, where each node represents one object and each edge is related to probabilities so that we can quantify the chances that a pair of nodes exit ( Khan et al, 2018b ; Ke et al, 2020 ). In neuroimaging, each node in an uncertain network refers to a brain region, and each edge refers to a probabilistic connection; this indicates the likelihood that a functional connection exists in the brain.…”

Section: Introductionmentioning

confidence: 99%

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Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

Zhou

et al. 2022

Front. Neurosci.

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The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.

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“…Uncertain graphs are undirected graphs with uncertainties represented as

Section: Methodsmentioning

confidence: 99%

“…The probability that a certain graph G ∈ W (

) is implied from an uncertain graph

, which is defined by Eq. 1 ( Khan et al, 2018b ; Ke et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

Zhou

et al. 2022

Front. Neurosci.

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“…Uncertain networks, i.e., graphs where each edge is associated with a probability of existence, have received a great deal of attention thanks to their expressivity and applicability in many real world contexts. Researchers have studied 𝑘-nearest neighbor queries [39,52], reachability queries [31], clustering [23], sampling [48], network design [30], and embedding [24], just to mention a few. Uncertainty in a network might arise due to noisy measurements [2], edge imputation using inference and prediction models [1,40], and explicit manipulation of edges, e.g., for privacy purposes [7].…”

Section: Introductionmentioning

confidence: 99%

Shortest paths and centrality in uncertain networks

Saha¹,

Brokkelkamp

Velaj

et al. 2021

Proc. VLDB Endow.

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Computing the shortest path between a pair of nodes is a fundamental graph primitive, which has critical applications in vehicle routing, finding functional pathways in biological networks, survivable network design, among many others. In this work, we study shortest-path queries over uncertain networks, i.e., graphs where every edge is associated with a probability of existence. We show that, for a given path, it is # P -hard to compute the probability of it being the shortest path, and we also derive other interesting properties highlighting the complexity of computing the Most Probable Shortest Paths (MPSPs). We thus devise sampling-based efficient algorithms, with end-to-end accuracy guarantees, to compute the MPSP. As a concrete application, we show how to compute a novel concept of betweenness centrality in an uncertain graph using MPSPs. Our thorough experimental results and rich real-world case studies on sensor networks and brain networks validate the effectiveness, efficiency, scalability, and usefulness of our solution.

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