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Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of community detection is to discover closely connected groups of entities in complex networks such as interest groups, proteins and vehicles in social, biological and transportation networks. Recently, autoencoders have become a popular technique to extract nonlinear relationships between nodes by learning their representation vectors through an encoder-decoder neural structure, which is beneficial to discovering communities with vague boundaries. However, most of the existing autoencoders take restoring a network's adjacency matrix as their objective, which puts emphasis on the first-order relationships between the nodes and neglects their higher-order relationships that may be more useful for community detection. In this paper, we propose a novel attentional-walk-based autoencoder (AWBA) which integrates random walk considering attentional coefficients between each pair of nodes into the encoder to mine their high-order relationships. First, the attention layers are added to the encoder to learn the influence of a node's different neighbors on it in encoding. Second, we develop a new random walk strategy that embeds the attention coefficients and the community membership of the nodes obtained by a seed-expansion-based clustering algorithm into the computation of the transition probability matrix to instill both low and high order relationships between the nodes into the representation vectors. The experimental results on synthetic and real-world networks verify the superiority of our algorithm over the baseline algorithms. [ABSTRACT FROM AUTHOR]

Community detection is an essential task in network analysis and is challenging due to the rapid growth of network scales. Recently, discovering communities from the local perspective of some specified nodes called seeds, rather than requiring the global information of the entire network, has become an alternative approach to addressing this challenge. Some seeding algorithms have been proposed in the literature for finding seeds, but many of them require an excessive amount of effort because of the global information or intensive computation involved. In our study, we formally summarize a unified framework for local seeding by considering only the local information of each node. In particular, both popular local seeding algorithms and new ones are instantiated from this unified framework by adopting different centrality metrics. We categorize these local seeding algorithms into three classes and compare them experimentally on a number of networks. The experiments demonstrate that the degree-based algorithms usually select the fewest seeds, while the denseness-based algorithms, except the one with node mass as the centrality metric, select the most seeds; using the conductance of the egonet as the centrality metric performs best in discovering communities with good quality; the core-based algorithms perform best overall considering all the evaluation metrics; and among the core-based algorithms, the one with the Jaccard index works best. The experimental results also reveal that all the seeding algorithms perform poorly in large networks, which indicates that discovering communities in large networks is still an open problem that urgently needs to be addressed. [ABSTRACT FROM AUTHOR]

Background. Heracleum sosnowskyi Manden. (family Umbelliferae = Apiaceae) is included in the lists of invasive species in many regions of Russia and European countries. Mericarps (fruits conventionally called seeds) of Heracleum L. spp. are characterized by an underdeveloped embryo and a complex morphophysiological type of dormancy; two-stage stratification is needed for their germination.Methods. In the period from 2014 to 2019, fruits of H. sosnowskyi were collected near settlements in Vyborg and Gatchina Districts, Leningrad Province; collecting was carried out from August through March. Seed size and 1000 seed weight were measured. Germination experiments were performed in the laboratory (22–24°C) monthly, from the time of collection in August until May. Seeds were germinated in Petri dishes on filter paper with three replications.Results. Large seeds on marginal umbellules of the central umbel were the largest in size. Their length was from 0.6 to 1.4 cm, width from 5 to 11 mm, and 1000 seed weight from 9 to 18 g. The length of smaller fruits was from 7 to 9 mm, width from 3 to 5 mm, and 1000 seed weight from 10.5 to 11.8 g. Germination of seeds collected in late summer was protracted. Under laboratory conditions, the emergence of the first seedlings was observed after 5–7 days. For the fruits stored in the laboratory, germination was reduced. Germination rate of stratified seeds varied from 55 to 99%.Conclusion. The biometric parameters of H. sosnowskyi mericarps were slightly affected by the place of growth and the year of maturation. Under laboratory conditions, H. sosnowskyi seeds collected in August showed germination rates from 3 to 15%. In years with a warm and long autumn, part of the fruit germinated in the year of maturation and managed to reach their juvenile state. Plump mericarps remained viable for at least three years. Sosnowsky’s hogweed fruits stored under laboratory conditions lost their germination ability within a year.

Community identification refers to the process of selecting dense clusters with sparse connections to the rest of the graph. These communities naturally overlap in many social networks, while each node can participate in several communities. Revealing these structures represents an important task in network analysis. In fact, it allows to understand the features of networks. However, many community detection approaches fail to provide communities with high quality and better ground truth correspondence in reasonable execution time. The key idea of this paper is to propose a coupled-seed expansion method for the overlapping community detection. Specifically, we construct a coupled-seed by choosing a node and its most similar neighbor and then expand this coupled-seed using a fitness function that improves the identification of local communities. The overlapping modularity, the F-score and the extended normalized mutual information measures are used to evaluate the proposed algorithm. The experimental results on 10 instances of real networks and four sets of LFR networks prove that the proposed method is effective and outperforms the existing algorithms (BigClam, OSLOM, SE, Demon, UMSTMO, LC, Ego-Splitting). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Local graph clustering methods aim to find small clusters in very large graphs. These methods take as input a graph and a seed node, and they return as output a good cluster in a running time that depends on the size of the output cluster but that is independent of the size of the input graph. In this paper, we adopt a statistical perspective on local graph clustering, and we analyze the performance of the ℓ1-regularized PageRank method (Fountoulakis et al., 2019) for the recovery of a single target cluster, given a seed node inside the cluster. Assuming the target cluster has been generated by a random model, we present two results. In the first, we show that the optimal support of ℓ1-regularized PageRank recovers the full target cluster, with bounded false positives. In the second, we show that if the seed node is connected solely to the target cluster then the optimal support of -regularized PageRank recovers exactly the target cluster. We also show empirically that lregularized PageRank has a state-of-the-art performance on many real graphs, demonstrating the superiority of the method. From a computational perspective, we show that the solution path of ℓ1-regularized PageRank is monotonic. This allows for the application of the forward stagewise algorithm, which approximates the entire solution path in running time that does not depend on the size of the whole graph. Finally, we show that ℓ1-regularized PageRank and approximate personalized PageRank (APPR) (Andersen et al., 2006), another very popular method for local graph clustering, are equivalent in the sense that we can lower and upper bound the output of one with the output of the other. Based on this relation, we establish for APPR similar results to those we establish for ℓl-regularized PageRank. [ABSTRACT FROM AUTHOR]

Most real-world information networks are heterogeneous, which contain multiple types of entities and relations between the entities. Large-scale and heterogeneity are the typical properties of heterogeneous information networks, and their community structures are often overlapping, complex, and diverse. The existing community detection algorithms without considering the above-mentioned properties may lead to low accuracy or high time complexity of community detection. In this paper, we study the multi-dimensional heterogeneous community detection problem for large-scale heterogeneous information networks with general topology structures. We define the concept of node-centric community and propose a multi-dimensional community detection algorithm referred as Hete_MESE. Specifically, we first specify one of the multiple entity types in heterogeneous information networks as community centric node type and extract the multiplex networks accordingly, and then, overlapping node-centric communities are detected based on the multiplex networks, which are considered as the seed communities to absorb the other entity types to generate the heterogeneous communities utilizing seed expansion. Taking heterogeneous academic network as an example, the effects of Hete_MESE are evaluated through extensive experiments, and the features of detected multi-dimensional academic communities are analyzed. The results demonstrate that Hete_MESE can accurately and effectively detect the meaningful overlapping and heterogeneous communities in heterogeneous information networks with general topologies from multiple dimensions. Moreover, Hete_MESE has linear time complexity, so it can be applied to large-scale heterogeneous information networks.

Abstract Community detection is an important research area for its necessity in understanding the hidden structure of complex networks. A vast variety of overlapping community detection methods have been proposed in the literature, and the local expansion method is gaining much attention for its success in dealing with large networks. However, the selection of seeds limits the quality of the identified communities for the method. The paper presents a similarity-based seeding method, in which lines and triangles are searched and selected as seeds. The proposed method selects the core of a seed by the influence of the vertices, and identifies other members of the seed by a novel function, which is a combination of vertex influence and similarity. Experimental results in synthetic networks show that the proposed seeding method outperforms other seeding methods in the state of the art. Experimental results in real-world networks with ground-truth communities show that the proposed approach outperforms other state of the art overlapping community detection algorithms, in particular, it is more than two orders of magnitude faster than the existing global algorithms with competitive quality, and it obtains much more accurate community structure than the current local algorithms without any priori information. Highlights • We propose an efficient and accurate overlapping community detection method. • We propose a vertex similarity measure that takes vertex influence into account. • We propose a seeding method that selects seeds by vertex influence and similarity. [ABSTRACT FROM AUTHOR]

Seed expansion in peanut is a complex biological process involving many gene regulatory pathways. MicroRNAs (miRNAs) play important regulatory roles in plant growth and development, but little is known about their functions during seed expansion, or how they contribute to seed expansion in different peanut lines. We examined seed miRNA expression patterns at 15 and 35 days after flowering (DAF) in two peanut eighth-generation recombinant inbred lines (RIL8); 8106, a medium-pod variety, and 8107, a super-pod variety. Using high-throughput sequencing, we identified 1,082 miRNAs in developing peanut seeds including 434 novel miRNAs. We identified 316 differentially expressed miRNAs by comparing expression levels between the two peanut lines. Interestingly, 24 miRNAs showed contrasting patterns of expression in the two RILs, and 149 miRNAs were expressed predominantly in only one RIL at 35 DAF. Also, potential target genes for some conserved and novel miRNAs were identified by degradome sequencing; target genes were predicted to be involved in auxin mediated signaling pathways and cell division. We validated the expression patterns of some representative miRNAs and 12 target genes by qPCR, and found negative correlations between the expression level of miRNAs and their targets. miR156e, miR159b, miR160a, miR164a, miR166b, miR168a, miR171n, miR172c-5p, and miR319d and their corresponding target genes may play key roles in seed expansion in peanut. The results of our study also provide novel insights into the dynamic changes in miRNAs that occur during peanut seed development, and increase our understanding of miRNA function in seed expansion.

Social network analysis is one of the key areas of research during modern times. The social network is growing with more users and the ties between them day by day. This reason brings out many research queries and new conclusions from this area. Overlapping community detection in the social network is one such research problem which has acquired interest among researchers nowadays. Earlier, the investigation was in finding out algorithms to detect communities in the network sequentially. There are many distinguished findings toward overlapping community detection. Due to the velocity of data in the current era, the available algorithms will be a bit sluggish in processing the data. The proposed algorithm uses parallel processing engine to resolve this delay problem in the current scenario. The algorithm in parallel finds out the superior seed set in the network and expands it in parallel to find out the community. The work shows amazing improvement in the runtime and also detects quality groups in the network. [ABSTRACT FROM AUTHOR]

Community plays an important role in the field of sociology, biology and especially in domains of computer science, where systems are often represented as networks. And community detection is of great importance in the domains. A community is a dense subgraph of the whole graph with more links between its members than between its members to the outside nodes, and nodes in the same community probably share common properties or play similar roles in the graph. Communities overlap when nodes in a graph belong to multiple communities. A vast variety of overlapping community detection methods have been proposed in the literature, and the local expansion method is one of the most successful techniques dealing with large networks. The paper presents a density-based seeding method, in which dense disjoint local clusters are searched and selected as seeds. The proposed method selects a seed by the total degree and density of local clusters utilizing merely local structures of the network. Furthermore, this paper proposes a novel community refining phase via minimizing the conductance of each community, through which the quality of identified communities is largely improved in linear time. Experimental results in synthetic networks show that the proposed seeding method outperforms other seeding methods in the state of the art and the proposed refining method largely enhances the quality of the identified communities. Experimental results in real graphs with ground-truth communities show that the proposed approach outperforms other state of the art overlapping community detection algorithms, in particular, it is more than two orders of magnitude faster than the existing global algorithms with higher quality, and it obtains much more accurate community structure than the current local algorithms without any priori information. [ABSTRACT FROM AUTHOR]

Seed expansion in peanut is a complex biological process involving many gene regulatory pathways. MicroRNAs (miRNAs) play important regulatory roles in plant growth and development, but little is known about their functions during seed expansion, or how they contribute to seed expansion in different peanut lines. We examined seed miRNA expression patterns at 15 and 35 days after flowering (DAF) in two peanut eighth-generation recombinant inbred lines (RIL8); 8106, a mediumpod variety, and 8107, a super-pod variety. Using high-throughput sequencing, we identified 1,082 miRNAs in developing peanut seeds including 434 novel miRNAs. We identified 316 differentially expressed miRNAs by comparing expression levels between the two peanut lines. Interestingly, 24 miRNAs showed contrasting patterns of expression in the two RILs, and 149 miRNAs were expressed predominantly in only one RIL at 35 DAF. Also, potential target genes for some conserved and novel miRNAs were identified by degradome sequencing; target genes were predicted to be involved in auxin mediated signaling pathways and cell division. We validated the expression patterns of some representative miRNAs and 12 target genes by qPCR, and found negative correlations between the expression level of miRNAs and their targets. miR156e, miR159b, miR160a, miR164a, miR166b, miR168a, miR171n, miR172c- 5p, and miR319d and their corresponding target genes may play key roles in seed expansion in peanut. The results of our study also provide novel insights into the dynamic changes in miRNAs that occur during peanut seed development, and increase our understanding of miRNA function in seed expansion. [ABSTRACT FROM AUTHOR]

Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage. [ABSTRACT FROM AUTHOR]

Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this paper, we propose an efficient overlapping community detection algorithm using a seed expansion approach. The key idea of our algorithm is to find good seeds, and then greedily expand these seeds based on a community metric. Within this seed expansion method, we investigate the problem of how to determine good seed nodes in a graph. In particular, we develop new seeding strategies for a personalized PageRank clustering scheme that optimizes the conductance community score. An important step in our method is the neighborhood inflation step where seeds are modified to represent their entire vertex neighborhood. Experimental results show that our seed expansion algorithm outperforms other state-of-the-art overlapping community detection methods in terms of producing cohesive clusters and identifying ground-truth communities. We also show that our new seeding strategies are better than existing strategies, and are thus effective in finding good overlapping communities in real-world networks. [ABSTRACT FROM PUBLISHER]