scholarly journals Mining communities and their descriptions on attributed graphs: a survey

2021 ◽  
Vol 35 (3) ◽  
pp. 661-687
Author(s):  
Martin Atzmueller ◽  
Stephan Günnemann ◽  
Albrecht Zimmermann
Keyword(s):  
Pattern Mining ◽  
Large Majority ◽  
Post Processing ◽  
Mining Communities ◽  
Attribute Weights ◽  
Research Fields ◽  
Attributed Graphs ◽  
Attribute Information ◽  
Conditional Attribute ◽  
Attribute Value

AbstractFinding communities that are not only relatively densely connected in a graph but that also show similar characteristics based on attribute information has drawn strong attention in the last years. There exists already a remarkable body of work that attempts to find communities in vertex-attributed graphs that are relatively homogeneous with respect to attribute values. Yet, it is scattered through different research fields and most of those publications fail to make the connection. In this paper, we identify important characteristics of the different approaches and place them into three broad categories: those that select descriptive attributes, related to clustering approaches, those that enumerate attribute-value combinations, related to pattern mining techniques, and those that identify conditional attribute weights, allowing for post-processing. We point out that the large majority of these techniques treat the same problem in terms of attribute representation, and are therefore interchangeable to a certain degree. In addition, different authors have found very similar algorithmic solutions to their respective problem.

scholarly journals Multiple Attribute Decision Making Based on Cross-Evaluation with Uncertain Decision Parameters

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Ding ◽  
Liang Liang ◽  
Min Yang ◽  
Huaqing Wu
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Service Companies ◽  
Attribute Weights ◽  
Research Fields ◽  
Multiple Attribute ◽  
Single Attribute ◽  
The Stability ◽  
The City

Multiple attribute decision making (MADM) problem is one of the most common and popular research fields in the theory of decision science. A variety of methods have been proposed to deal with such problems. Nevertheless, many of them assumed that attribute weights are determined by different types of additional preference information which will result in subjective decision making. In order to solve such problems, in this paper, we propose a novel MADM approach based on cross-evaluation with uncertain parameters. Specifically, the proposed approach assumes that all attribute weights are uncertain. It can overcome the drawback in prior research that the alternatives’ ranking may be determined by a single attribute with an overestimated weight. In addition, the proposed method can also balance the mean and deviation of each alternative’s cross-evaluation score to guarantee the stability of evaluation. Then, this method is extended to a more generalized situation where the attribute values are also uncertain. Finally, we illustrate the applicability of the proposed method by revisiting two reported studies and by a case study on the selection of community service companies in the city of Hefei in China.

scholarly journals Using Decision Analysis to Integrate Habitat and Community Values for Coastal Resilience Planning

2021 ◽  
Author(s):  
David M. Martin ◽  
Jackie A. Specht ◽  
Michelle R. Canick ◽  
Kelly L. Leo ◽  
Kathleen Freeman
Keyword(s):  
Decision Analysis ◽  
Critical Infrastructure ◽  
Wetland Loss ◽  
Ecological Knowledge ◽  
Value Functions ◽  
Economic Knowledge ◽  
Eastern Shore ◽  
Attribute Weights ◽  
Flood Impacts ◽  
Attribute Value

AbstractDecision analysis is applied to habitat and community resilience planning in Maryland, USA. Sea level rise is causing wetland loss and increased flooding in coastal areas. A team at The Nature Conservancy analyzed a decision to identify high-value conservation planning units across Maryland’s Lower Eastern Shore. The team selected two fundamental objectives: minimize habitat loss and minimize community flood impacts. Sub-objectives included habitat function, habitat migration potential, critical infrastructure, and social vulnerability. Spatial attributes were selected based on ecological knowledge about habitat and socio-economic knowledge about sustaining populations in flood-prone areas. Seven planning units were developed across the Lower Eastern Shore. Single-attribute value functions determined the overall value of each unit per attribute, whereas multi-attribute value functions determined the overall value of each unit for all fundamental objectives. Sensitivity analysis incorporated data adjustments based on different flood scenarios and unit sizes, and variation in attribute weights associated with the multi-attribute value function. The Pareto efficiency principle revealed tradeoffs between units for habitat protection and management and community engagement and adaptation. Results indicate that four units are Pareto efficient under different sensitivity iterations and they trade off value in the fundamental objectives, whereas one unit provides the highest combined habitat and community value. This research guided thinking about equity in decision making and targeting conservation actions at local scales. The approach and methods can be used to inform conservation decisions in other similar contexts.

Exploiting higher-order patterns for community detection in attributed graphs

2020 ◽  
pp. 1-12
Author(s):  
Lun Hu ◽  
Xiangyu Pan ◽  
Hong Yan ◽  
Pengwei Hu ◽  
Tiantian He
Keyword(s):  
Community Detection ◽  
Higher Order ◽  
Challenging Problem ◽  
Network Systems ◽  
Attributed Graphs ◽  
Attribute Information ◽  
Node Attributes ◽  
Real World Datasets ◽  
Different Characteristics ◽  
Do So

As a fundamental task in cluster analysis, community detection is crucial for the understanding of complex network systems in many disciplines such as biology and sociology. Recently, due to the increase in the richness and variety of attribute information associated with individual nodes, detecting communities in attributed graphs becomes a more challenging problem. Most existing works focus on the similarity between pairwise nodes in terms of both structural and attribute information while ignoring the higher-order patterns involving more than two nodes. In this paper, we explore the possibility of making use of higher-order information in attributed graphs to detect communities. To do so, we first compose tensors to specifically model the higher-order patterns of interest from the aspects of network structures and node attributes, and then propose a novel algorithm to capture these patterns for community detection. Extensive experiments on several real-world datasets with varying sizes and different characteristics of attribute information demonstrated the promising performance of our algorithm.

scholarly journals Integrated Decision-Making Method for Heterogeneous Attributes Based on Probabilistic Linguistic Cross-Entropy and Priority Relations

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1009
Author(s):  
Lei Wang ◽  
Huifeng Xue
Keyword(s):  
Decision Making ◽  
Synthesis Method ◽  
Cross Entropy ◽  
Engineering Practice ◽  
Final Decision ◽  
Qualitative And Quantitative ◽  
Advantages And Disadvantages ◽  
Attribute Weights ◽  
Attribute Weight ◽  
Attribute Information

The meta-synthesis method has achieved good results in China’s aerospace engineering and population economic regulation. This theoretical achievement obtained from engineering practice becomes an effective way to solve complex decision-making problems. The meta-synthesis method obtains the final decision-making result by comprehensively considering qualitative and quantitative criteria and gathering multivariate heterogeneous attribute information. In view of the broad application of entropy theory in quantitative evaluation and fuzzy decision-making, this paper proposes a meta-synthesis decision-making method based on probabilistic linguistic cross-entropy and priority relations for multicriteria decision-making problems including qualitative and quantitative multivariate heterogeneous attribute information. First, the quantitative attribute weight is calculated based on the entropy weight method, and the qualitative attribute weight is calculated by considering the individual effects and interactions of the probabilistic linguistic term sets under qualitative attributes comprehensively through probabilistic linguistic entropy and cross-entropy. Then, the weight preference coefficient is used to integrate the qualitative and quantitative heterogeneous attribute weights to obtain standardized processing weight information, and, on the basis of the 0–1 priority relation matrix, we compare and analyze the advantages and disadvantages of alternatives under all criteria and obtain an overall ranking result of the alternatives. Finally, the effectiveness and superiority of the proposed method are verified by a comparative analysis of a numerical example and the decision-making method.

An Improved Value Reduction Algorithm Based on Core Value

2011 ◽  
Vol 187 ◽  
pp. 216-220
Author(s):  
Wei Du ◽  
Wei Wang
Keyword(s):  
Information System ◽  
Decision Rule ◽  
Reduction Algorithm ◽  
Core Value ◽  
Conditional Attribute ◽  
Improved Algorithm ◽  
Attribute Value

Value reduction algorithm can filter and delete redundant conditional attribute value, so as to obtain decision rule of information system with least conditional attribute values. Based on the introduction of basic value reduction algorithm, the paper supplemented functions. Aiming at the circumstance of there is no repeated record and no conflict after deleting some attributes of a record, the algorithm supplement it. The example of value reduction based on the improved algorithm illustrated that it is an effective value reduction algorithm and an important supplement of basic value reduction algorithm.

scholarly journals High Influencing Pattern Discovery over Time Series Data

2021 ◽  
Vol 10 (10) ◽  
pp. 696
Author(s):  
Dianwu Fang ◽  
Lizhen Wang ◽  
Jialong Wang ◽  
Meijiao Wang
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Spatial Distance ◽  
Series Data ◽  
Analytic Hierarchy ◽  
Location Pattern ◽  
Attribute Weights ◽  
Downward Closure ◽  
High Influence

A spatial co-location pattern denotes a subset of spatial features whose instances frequently appear nearby. High influence co-location pattern mining is used to find co-location patterns with high influence in specific aspects. Studies of such pattern mining usually rely on spatial distance for measuring nearness between instances, a method that cannot be applied to an influence propagation process concluded from epidemic dispersal scenarios. To discover meaningful patterns by using fruitful results in this field, we extend existing approaches and propose a mining framework. We first defined a new concept of proximity to depict semantic nearness between instances of distinct features, thus applying a star-shaped materialized model to mine influencing patterns. Then, we designed attribute descriptors to perceive attributes of instances and edges from time series data, and we calculated the attribute weights via an analytic hierarchy process, thereby computing the influence between instances and the influence of features in influencing patterns. Next, we constructed influencing metrics and set a threshold to discover high influencing patterns. Since the metrics do not satisfy the downward closure property, we propose two improved algorithms to boost efficiency. Extensive experiments conducted on real and synthetic datasets verified the effectiveness, efficiency, and scalability of our method.

scholarly journals Inductive Link Prediction for Nodes Having Only Attribute Information

2020 ◽  
Author(s):  
Yu Hao ◽  
Xin Cao ◽  
Yixiang Fang ◽  
Xike Xie ◽  
Sibo Wang
Keyword(s):  
Link Prediction ◽  
Fundamental Problem ◽  
Inductive Link ◽  
Structure Information ◽  
Proposed Model ◽  
Attributed Graphs ◽  
Attribute Information ◽  
Benchmark Datasets ◽  
Alignment Mechanism ◽  
Model Training

Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which consists of three components: two node embedding encoders and one alignment mechanism. The two encoders aim to output the attribute-oriented node embedding and the structure-oriented node embedding, and the alignment mechanism aligns the two types of embeddings to build the connections between the attributes and links. Our model DEAL is versatile in the sense that it works for both inductive and transductive link prediction. Extensive experiments on several benchmark datasets show that our proposed model significantly outperforms existing inductive link prediction methods, and also outperforms the state-of-the-art methods on transductive link prediction.

scholarly journals Non-Dual Multi-Granulation Neutrosophic Rough Set with Applications

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 910 ◽  
Author(s):  
Chunxin Bo ◽  
Xiaohong Zhang ◽  
Songtao Shao
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Information Aggregation ◽  
Management Decision ◽  
Complex Data ◽  
Multiple Attribute ◽  
Attribute Information ◽  
Multi Attribute Decision Making ◽  
Neutrosophic Number ◽  
Attribute Value

Multi-attribute decision-making (MADM) is a part of management decision-making and an important branch of the modern decision theory and method. MADM focuses on the decision problem of discrete and finite decision schemes. Uncertain MADM is an extension and development of classical multi-attribute decision making theory. When the attribute value of MADM is shown by neutrosophic number, that is, the attribute value is complex data and needs three values to express, it is called the MADM problem in which the attribute values are neutrosophic numbers. However, in practical MADM problems, to minimize errors in individual decision making, we need to consider the ideas of many people and synthesize their opinions. Therefore, it is of great significance to study the method of attribute information aggregation. In this paper, we proposed a new theory—non-dual multi-granulation neutrosophic rough set (MS)—to aggregate multiple attribute information and solve a multi-attribute group decision-making (MGDM) problem where the attribute values are neutrosophic numbers. First, we defined two kinds of non-dual MS models, intersection-type MS and union-type MS. Additionally, their properties are studied. Then the relationships between MS, non-dual MS, neutrosophic rough set (NRS) based on neutrosophic intersection (union) relationship, and NRS based on neutrosophic transitive closure relation of union relationship are outlined, and a figure is given to show them directly. Finally, the definition of non-dual MS on two universes is given and we use it to solve a MGDM problem with a neutrosophic number as the attribute value.

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