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The state-of-the-art methods designed for overlapping community detection are limited by their high execution time as in CPM or the need to provide some parameters like the number of communities in Bigclam and Nise_sph, which is a nontrivial information. Hence, there is a need to develop the accuracy that represents the primordial goal, where the actual state-of-the-art methods do not succeed to achieve high correspondence with the ground truth for many instances of networks. The paper aims to discuss this issue.

The authors offer a new method that explore the union of all maximum spanning trees (UMST) and models the strength of links between nodes. Also, each node in the UMST is linked with its most similar neighbor. From this model, the authors extract local community for each node, and then they combine the produced communities according to their number of shared nodes.

The experiments on eight real-world data sets and four sets of artificial networks show that the proposed method achieves obvious improvements over four state-of-the-art (BigClam, OSLOM, Demon, SE, DMST and ST) methods in terms of the F-score and ONMI for the networks with ground truth (Amazon, Youtube, LiveJournal and Orkut). Also, for the other networks, it provides communities with a good overlapping modularity.

In this paper, the authors investigate the UMST for the overlapping community detection.

Coordinated supply chain is concerned with managing dependencies between various members and joint efforts of all members to achieve mutually defined goals. The purpose of this paper is to identify and prioritize the factors for a coordinated supply chain.

Analytic hierarchy process (AHP) is used to prioritize the factors for a coordinated supply chain. By using AHP global desirability index of five strategic factors for coordinated supply chain their 23 sub‐factors have been calculated and compared.

In this study 23 factors affecting coordination in a supply chain are considered. These factors are grouped under five strategic factors such as top management commitment, mutual understanding, relationship and decision‐making, flow of information and organizational factors. It is observed that the global weightage of top management commitment is highest among strategic factors and agreed vision and goal of supply chain members among sub factors.

AHP has some limitations. A major limitation is that the rating scale used in the AHP analysis is conceptual and there are chances of bias while giving relative weightage to different factors.

Top management should strive for an agreed vision and a common goal among all members of the supply chain to have effective coordination.

This study prioritizes factors for a coordinated supply chain in the Indian context and the findings will be significant while formulating strategies.

This paper presents an overview of three information‐theoretic methods, which have been used extensively in many areas such as signal/image processing, pattern recognition and statistical inference. These are: the maximum entropy (ME), minimum cross‐entropy (MCE) and mutual information (MI) methods. The development history of these techniques is reviewed, their essential philosophy is explained, and typical applications, supported by simulation results, are discussed.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.

Green economy and economic development with high quality have set higher requirements for the development of the urban logistics industry. It can grasp the recent development level of the urban logistics industry by measuring its environmental efficiency to guide its future development direction. The purpose of this study is to improve the environmental efficiency and development level of the urban logistics industry by using a reasonable evaluation method.

This paper uses information entropy to directly aggregate index weights from different models to acquire comprehensive index weights (CIWs) for calculating peer-evaluation efficiency. Then, we weight self and peer-efficiencies to obtain final efficiency. The environmental efficiencies of the urban logistics industry in Anhui Province in 2019 are obtained according to the above method.

Several findings are summarized below. The logistics industry in Anhui is in urgent need of improving environmental efficiency. The environmental efficiency of the logistics industry in North Anhui is the highest one, showing that the logistics industry in North Anhui has achieved a relative balance between economic development and environmental protection. Their final cross-efficiency values based on the CIWs are smaller than those based on the comprehensive efficiency. And the environmental efficiency of almost all urban logistics industries is lower than its economic efficiency. The findings show that the proposed method is feasible and more reasonable. More economic implications and suggestions are proposed.

This paper proposes an extended cross-efficiency evaluation method based on information entropy to measure the environmental efficiency of the urban logistics industry, effectively avoiding the overestimation of efficiency results.

Summarization of an entire web site with diverse content may lead to a summary heavily biased towards the site's dominant topics. The purpose of this paper is to present a novel topic‐based framework to address this problem.

A two‐stage framework is proposed. The first stage identifies the main topics covered in a web site via clustering and the second stage summarizes each topic separately. The proposed system is evaluated by a user study and compared with the single‐topic summarization approach.

The user study demonstrates that the clustering‐summarization approach statistically significantly outperforms the plain summarization approach in the multi‐topic web site summarization task. Text‐based clustering based on selecting features with high variance over web pages is reliable; outgoing links are useful if a rich set of cross links is available.

More sophisticated clustering methods than those used in this study are worth investigating. The proposed method should be tested on web content that is less structured than organizational web sites, for example blogs.

The proposed summarization framework can be applied to the effective organization of search engine results and faceted or topical browsing of large web sites.

Several key components are integrated for web site summarization for the first time, including feature selection and link analysis, key phrase and key sentence extraction. Insight into the contributions of links and content to topic‐based summarization was gained. A classification approach is used to minimize the number of parameters.

Organizational climate is an essential dynamic to leverage in salesforce performance. This study aims to develop a model that explores the determinants of independent manufacturers’ representatives’ (i.e. IMRs’) intentions to comply with their principals’ requests for additional tasking. Using agency theory, the authors explore the application of behavior and outcome-based controls upon dyadic manufacturer-IMR relationships for these additional performance/task requests.

Data from over 1,000 US-based IMRs were used to test two constructs; inter-organizational climate and perceptions of mutual satisfaction within the agency-principal dyad. Compliance behaviors tested were IMRs’ intentions to engage in non-selling-related tasks and intentions to allocate additional selling time to principals’ products. The following four exogenous controls were tested: perceived goal congruence between IMRs and principals; IMRs’ perceptions of principals’ expertise; mutual communications between IMRs and principals in the supply chain dyad; resources and sales support programs provided by principals to IMRs; and IMRs’ perceptions of the adequacy and fairness of the principals’ compensation plans.

Two constructs – inter-organizational climate and perceptions of mutual satisfaction with the agency-principal dyad – mediated the effects of exogenous sales controls on two compliance behaviors. The model’s data were analyzed using Partial least squares structural equation modeling (PLS-SEM). A marker variable was deployed to check for common method variance also supported using the Partial least squares (PLS) factor solution. Most variables demonstrated significant direct and mediated effects on each compliance behavior. Variables that emphasized behavioral-based controls dominated intentions for IMRs to engage in non-selling tasks. The principal commission structure, the only sales outcome-based control in the study, most influenced IMRs’ intentions to commit additional sales time to their principals’ products.

This study only examined the intentions of IMRs to engage in additional selling activities and their intention to engage in non-selling tasks. Principals may desire longer-term commitments from IMRs. The model developed here can be modified to capture additional behavioral and attitudinal outcomes including, for example, the exit intentions of IMRs.

Principals are well-advised to foster a positive inter-organizational climate that fuels perceptions of mutually satisfying working relationships with their IMRs. These mutually satisfying working relationships can, by themselves, positively influence IMRs to acquiesce to reasonable requests made by principals. This advice appears to be particularly crucial when asking IMRs to engage in additional non-selling tasks. The total pattern of path estimates points to the conclusion that capable sales control plays an important role in fostering positive inter-organizational climates. The inter-organizational climate – mutual satisfaction link proved crucial as a mediator of the impact of sales controls on IMRs’ behavioral compliance intentions.

Knowing the impact of sales controls on IMR’s affords businesses the ability to use these controls for behavioral compliance intentions on non-selling tasks.