Speaker-listener Label Propagation Algorithm Research Articles

An approach to co-medication mechanism mining of Chinese Materia Medica and western medicines based on complex networks with the multi-source heterogeneous information

The co-medication strategy of Chinese Materia Medica (CMM) and western medicines under the holistic integrative medicine (HIM) model offers the advantages of increased efficacy and reduced toxicity, particularly in clinical practice for treating complex and challenging diseases. However, due to the mutual fragmentation of basic theories and principles of medication decision-making between traditional Chinese medicine (TCM) and western medicine, research on the mechanisms for the combined use of CMM and western medicines has become a challenging problem in modern clinical medicine research. Traditional drug combination mining models encounter numerous bottlenecks when dealing with this complex decision-making problem with multi-source information characteristics. This paper aims to mine the optimal drug combination strategy from a real-life decision-making scenario of co-medication between CMM and western medicines. It abstracts this realistic problem as a class of complex network optimal overlapping community detection problem, focusing on solving key difficulties such as the inability of existing drug complex network models to handle the multi-source information of drugs and the randomness in the community detection process. We integrate co-medication prescription information with attribute information to construct the speaker-listener label propagation algorithm based on semantic attribute features (SA-SLPA) and develop a new medicine combination identification strategy based on weighted complex networks. Using 6671 clinically real prescriptions, we conclude that the detection quality of the SA-SLPA method proposed in this paper is improved by up to 78% compared to existing methods. This improvement illustrates the scientificity and effectiveness of the model in this paper and provides new ideas for the application of theoretical models and methods of interdisciplinary disciplines such as decision science, data science, and computational science in the field of modern medicine.

Associations between quality of care in informal provider networks and nursing home admissions in Germany: results of a retrospective cohort study using German health claims data

BackgroundHigh-quality outpatient medical care can prevent care-dependent people from having to move to a nursing home. Quality of care is determined by the behaviour and cooperation of providers, which, when sharing patient collectives, can be understood as functionally defined informal provider networks (PN). There is still a lack of knowledge about the relationship between the quality of care in the interaction among service providers as a structural characteristic of a PN and nursing home admissions (NHA). We therefore examined associations between treatment quality, compositional characteristics, such as the number of general practicioners in the PN and NHA.MethodsGerman statutory health insurance claims data from 2006 to 2016 was used in a retrospective cohort study. The observation cohort comprised community-dwelling people ≥ 65 years of age who initially became care-dependent in 2006 (n = 117,942). PN were constructed using the Speaker-Listener Label Propagation Algorithm. The quality of care provided by such networks was assessed by further including all people ≥ 65 years of age who were cared for by service providers of the observation cohort. Quality of care in the PN was measured using 67 quality indicators (QI). Event-time series analyses in three proportional hazard models, taking into account random effects, determined the association of treatment quality characteristics and compositional characteristics of the PN with NHA.Results35,540 admissions occurred in 406 PN. The majority of QI and individual predictors show significant associations with NHA, as well as a few compositional characteristics of the PN. Out of 67 QI, 37 were significant in two of three models, 19 of which were associated with a lower risk and 18 with an increased risk for NHA.ConclusionsAssociations between quality characteristics of the PN and the risk of NHA constitute a relevant influence as they remain significant when controlled for individual predictors. Most compositional characteristics had no influence on NHA. Aspects of treatment quality thus do play a role in determining how long care-dependent people continue to live at home after onset of care-dependency. The results contribute to revealing informal relationships between service providers that constitute a special characteristic of the German health care system and to the identification of starting points for further education in high quality treatment of selected populations and in formalizing care collaborations by joining voluntary PN. Further, sensitising service providers to the evaluation of care processes and to reflecting on the relevance of their role in PN can improve quality development and outcomes.

A Manufacturing Network Modeling and Evolution Characterizing Approach for Self-Organization Among Distributed MSMEs Under Social Manufacturing Context

In the manufacturing industry, cross-enterprise resource sharing has emerged among micro-and-small-scale manufacturing enterprises (called social manufacturing nodes, SMNs) with similar manufacturing resources. In this context, social manufacturing is proposed to promote resource sharing among SMNs through order sharing in manufacturing communities (MCs) on the network platform. In social manufacturing, SMNs are geographically distributed and peer-to-peer, and MCs are formed by the self-organization among SMNs. However, because of the distributed and peer-to-peer characteristic of SMNs, the efficiency of SMNs self-organizing into MCs is relatively low, and the scope of self-organization is also relatively narrow. After MCs are formed, the structure of MCs is time-varying, and the evolution information of MCs is helpful for the smooth operation of the network platform. For these problems, this paper proposes a manufacturing network modeling and evolution characterizing approach. Firstly, distributed SMNs are clustered into overlapping MCs by the speaker-listener label propagation algorithm. Based on the clustering result, SMNs are recommended to each other as potential partners, by which they can quickly self-organize into MCs. On the other hand, seven fundamental events are defined to characterize the evolution of MCs on the network platform. From the evolution of MCs, the manager of the network platform get useful information for the smooth operation of the network platform. The feasibility of the proposed approach is verified by a simulation case.

Uncovering Community Structure in Geospatial Networks: A Case Study of Urban Bus Networks in Hangzhou, China

Abstract. Complex systems are very often organized under the form of networks, where nodes and edges are embedded in the geographic space in which human beings live. A geospatial network is a complex network that includes road networks, flight connections, physical internet infrastructures, and power grids, etc. The properties of geographic spaces and network topologies are key to the geospatial network function and evolution. It is crucial to characterize and understand the structure and evolution of these geospatial networks for different domains, ranging from social sciences to geographic sciences. Community structures, whose nodes often cluster into tightly knit groups (with a high density of within-group edges and a low density of between-group edges), have been given much attention because of their significance in analyzing complex geospatial networks. Community structures, which account for the functionality of a complex system in geographic space, represent a partition (disjoint community) or cover (overlapping community) of a geospatial network that is expected to have an optimal solution. Community structure found in geospatial network, such as those present in networks capturing the movements of people or animals, are subject to environmental factors, physical constraints, and other spatial influences. Therefore, communities in spatial networks often have geo-graphic meanings such as neighborhood boundaries, urban structures, and habitat territories. Moreover, the influence of geospatial indicators on the detection of community structure must not be overlooked.Compared with disjoint community structures, overlapping community structures are more ubiquitous community structures, which represent the fundamental characteristic of any complex network form in the physical world. In a geospatial network, community structures represent the different functions or structural units of a geographic space. Furthermore, overlapping communities are the result of the network evolution process, which represent the share channel of a neighbouring community. In geographical space, overlapping communities are always in the middle of two different communities. Spatially and functionally, overlapping communities are the bridge in communication between neighbouring communities. Therefore, the detection of overlapping communities in geospatial networks is of great significance. Although there are many methods for the detection of overlapping community structures, existing algorithms usually focus on network topology characteristics and ignore the effects of geospatial attributes on overlapping community structures.Hence, in this paper, we proposed a new methodology for uncovering communities in geospatial networks. The focus of our methodology is the spatially constrained speaker-listener label propagation algorithm (SLPA), which adds spatial resistance and spatial adjacency relations to the community detection algorithm. Furthermore, we make a case study using urban bus network in Hangzhou, China. The results show that the algorithm is efficient and reasonable to discovering overlapping community structures in geospatial networks. Overall, this study will help to understand and monitor the spatial interaction development of geographic phenomena and explore the functional characteristics of the urban complex system that are useful and meaningful for policy makers and planners.

$\mathcal{K}$ -Lowest-Influence Overlapping Nodes Based Community Detection in Complex Networks

Community detection is a classic and essential task in complex network analysis which aims at understanding their structural properties and functional organization. Community detection based on overlapping nodes has become one of the most popular methods in recent years. But, how to define the overlapping nodes in the real network is an important job. In this paper, we use the speaker-listener label propagation algorithm to find overlapping nodes. Second, we proposed a new metric which is based on local and global attributes to measure the node influence so as to evaluate the overlapping nodes’ importance. Community detection is realized with the $k$ -lowest-influence overlapping nodes deleting. Then, we assign the removed nodes into specific communities by voting to find the final community structure. The voting strategy is based on choosing the specific communities containing most of their neighbors. Finally, the extensive experiments on real-world networks demonstrate that our proposed method improves the quality of community detection methods and show both the effectiveness and efficiency of the method.

A parallel self-organizing overlapping community detection algorithm based on swarm intelligence for large scale complex networks

Community detection is a critical task for complex network analysis. It helps us to understand the properties of the system that a complex network represents and has significance to a wide range of applications. Though a large number of algorithms have been developed, the detection of overlapping communities from large scale and (or) dynamic networks still remains challenging. In this paper, a Parallel Self-organizing Overlapping Community Detection (PSOCD) algorithm ground on the idea of swarm intelligence is proposed. The PSOCD is designed based on the concept of swarm intelligence system where an analyzed network is treated as a decentralized, self-organized, and self-evolving systems, in which each vertex acts iteratively to join to or leave from communities based on a set of predefined simple vertex action rules. The algorithm is implemented on a distributed graph processing platform named Giraph++; therefore it is capable of analyzing large scale networks. The algorithm is also able to handle overlapping community detection well because a vertex can naturally joins to multiple communities simultaneously. Moreover, if some vertexes and edges are added to or deleted from the analyzed network, the algorithm only needs to adjust community assignments of affected vertexes in the same way as its finding joining communities for a vertex, i.e., it inherently supports dynamic network analysis. The proposed PSOCD is evaluated using a number of variety large scale synthesized and real world networks. Experimental results indicate that the proposed algorithm can effectively discover overlapping communities on large-scale network and the quality of its detected overlapping community structures is superior to two state-of-the-art algorithms, namely Speaker Listener Label Propagation Algorithm (SLPA) and Order Statistics Local Optimization Method (OSLOM), especially on high overlapping density networks and (or) high overlapping diversity networks.

A REVIEW ON OVERLAPPING AND NON-OVERLAPPING COMMUNITY DETECTION ALGORITHMS FOR SOCIAL NETWORK ANALYTICS

Community detection is a common problem that exists in the data graph analytics as the social networks analytics. In the context of social networks, community detection is aimed at to find a group or community that has connectedness between individuals (nodes) with high-intensity interaction. In general, types of group on the social networking can be divided into the overlapping and non-overlapping. We provide an overview of some of the algorithms of overlapping and non-overlapping community detections available today to perform an analysis or a breakdown of the data of social networking. The algorithms for overlapping community detection are: (1) local seed selection algorithm; (2) seed set expansion algorithm; (3) speaker listener label propagation algorithm (SPLA) and the algorithms for detection of non-overlapping community are: (4) multithreaded

Parallelizing SLPA for Scalable Overlapping Community Detection

Communities in networks are groups of nodes whose connections to the nodes in a community are stronger than with the nodes in the rest of the network. Quite often nodes participate in multiple communities; that is, communities can overlap. In this paper, we first analyze what other researchers have done to utilize high performance computing to perform efficient community detection in social, biological, and other networks. We note that detection of overlapping communities is more computationally intensive than disjoint community detection, and the former presents new challenges that algorithm designers have to face. Moreover, the efficiency of many existing algorithms grows superlinearly with the network size making them unsuitable to process large datasets. We use the Speaker-Listener Label Propagation Algorithm (SLPA) as the basis for our parallel overlapping community detection implementation. SLPA provides near linear time overlapping community detection and is well suited for parallelization. We explore the benefits of a multithreaded programming paradigm and show that it yields a significant performance gain over sequential execution while preserving the high quality of community detection. The algorithm was tested on four real-world datasets with up to 5.5 million nodes and 170 million edges. In order to assess the quality of community detection, at least 4 different metrics were used for each of the datasets.

The Effects of Centrality Ordering in Label Propagation for Community Detection

In many cases randomness in community detection algorithms has been avoided due to issues with stability. Indeed replacing random ordering with centrality rankings has improved the performance of some techniques such as Label Propagation Algorithms. This study evaluates the effects of such orderings on the Speaker-listener Label Propagation Algorithm or SLPA, a modification of LPA which has already been stabilized through alternate means. This study demonstrates that in cases where stability has been achieved without eliminating randomness, the result of removing random ordering is over fitting and bias. The results of testing seven various measures of centrality in conjunction with SLPA across five social network graphs indicate that while certain measures outperform random orderings on certain graphs, random orderings have the highest overall accuracy. This is particularly true when strict orderings are used in each run. These results indicate that the more evenly distributed solution space which results from complete random ordering is more valuable than the more targeted search that results from centrality orderings.

Overlapping Community Detection in Dynamic Networks

Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, LabelRankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping communities when compared to other community detection algorithms. SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network.

Finding Statistically Significant Communities in Networks with Weighted Label Propagation

Various networks exist in the world today including biological, social, information, and communication networks with the Internet as the largest network of all. One salient structural feature of these networks is the formation of groups or communities of vertices that tend to be more connected to each other within the same group than to those outside. Therefore, the detection of these communities is a topic of great interest and importance in many applications and different algorithms including label propagation have been developed for such purpose. Speaker-listener label propagation algorithm (SLPA) enjoys almost linear time complexity, so desirable in dealing with large networks. As an extension of SLPA, this study presented a novel weighted label propagation algorithm (WLPA), which was tested on four real world social networks with known community structures including the famous Zachary's karate club network. Wilcoxon tests on the communities found in the karate club network by WLPA demonstrated an improved statistical significance over SLPA. Withthehelp of Wilcoxon tests again, we were able to determine the best possible formation of two communities in this network relative to the ground truth partition, which could be used as a new benchmark for assessing community detection algorithms. Finally WLPA predicted better communities than SLPA in two of the three additional real social networks, when compared to the ground truth.