MONITORING MAMMAL COMMUNITY SHIFTS ACROSS SILVICULTURAL TREATMENTS UTILIZING CAMERA TRAPS AND THE DEVELOPMENT OF INVERTEBRATE- DERIVED DNA IN HARDWOOD FORESTS OF NORTH AMERICA

Mammal distribution and diversity is quickly changing as humans modify the landscape. In particular, silviculture, which is the practice of controlling the growth, structure, and quality of forests to meet the needs of society and the landowner, influences the habitat usage of mammals. Utilizing camera traps, I monitored shifts in mammal communities across different silviculture treatments in the northern hardwood forests of the Great Lakes region in North America. I assessed the community composition across six canopy treatments and three understory treatments with a total of 2,018 active camera trap nights with 3,321 detections over the course of 147 days. For canopy treatments, high canopy cover shelterwood had the largest positive influence of mammal detection while clearcut showed a negative influence of mammal detection. For understory treatments, artificial tip-up and scarification had higher mammal detection compared to control. Within areas with a history of disturbance it may be beneficial to the mammal communities to include small disturbances, such as those created by silviculture treatments, as local species are likely disturbance-adapted. Camera traps alone may miss part of the mammal community. To monitor a full community, other techniques need to be considered, such as invertebrate derived DNA (iDNA). iDNA, is emerging as a novel tool which utilizes genomic technologies to monitor and assess mammal communities. Some invertebrates ingest their host’s DNA as they feed, which then allows researchers to extract the host’s DNA and sequence it. By doing so, the researchers can then create a more complete image of mammal community compositions. This technique has been widely used in tropical zones to monitor mammal community compositions; however, it can be adapted to be used in temperate zones by utilizing ticks and mosquitoes. To adapt this technique, one must understand the environmental influences on invertebrate collection. I investigated the environmental influences on mosquito collection success by running linear regression models. Through running the linear regression models, I found that the canopy cover and time of the month had the largest influence on the collection of female mosquitoes, while tick collection was possibly influenced by the harshness of the winter before

Evidential Label Propagation Algorithm for Graphs

Community detection has attracted considerable attention crossing many areas as it can be used for discovering the structure and features of complex networks. With the increasing size of social networks in real world, community detection approaches should be fast and accurate. The Label Propagation Algorithm (LPA) is known to be one of the near-linear solutions and benefits of easy implementation, thus it forms a good basis for efficient community detection methods. In this paper, we extend the update rule and propagation criterion of LPA in the framework of belief functions. A new community detection approach, called Evidential Label Propagation (ELP), is proposed as an enhanced version of conventional LPA. The node influence is first defined to guide the propagation process. The plausibility is used to determine the domain label of each node. The update order of nodes is discussed to improve the robustness of the method. ELP algorithm will converge after the domain labels of all the nodes become unchanged. The mass assignments are calculated finally as memberships of nodes. The overlapping nodes and outliers can be detected simultaneously through the proposed method. The experimental results demonstrate the effectiveness of ELP.Comment: 19th International Conference on Information Fusion, Jul 2016, Heidelber, Franc

LATTE: Application Oriented Social Network Embedding

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector. However, due to the detachment of embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection or information diffusion. In this paper, we propose study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, in this paper, we propose a novel network embedding framework, namely the "appLicAtion orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network structure can be applied to compute the node "diffusive proximity" scores, which capture both local and global network structures. Based on these computed scores, Latte learns the network representation feature vectors by extending the autoencoder model model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets, and the experimental results have demonstrated the outstanding performance of Latte in learning the representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl

Compressing networks with super nodes

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network to be partitioned into a smaller network of 'super nodes', each super node comprising one or more nodes in the original network. To define the seeds of our super nodes, we apply the 'CoreHD' ranking from dismantling and decycling. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity, more stable across multiple (stochastic) runs within and between community detection algorithms, and overlap well with the results obtained using the full network

Local Edge Betweenness based Label Propagation for Community Detection in Complex Networks

Nowadays, identification and detection community structures in complex networks is an important factor in extracting useful information from networks. Label propagation algorithm with near linear-time complexity is one of the most popular methods for detecting community structures, yet its uncertainty and randomness is a defective factor. Merging LPA with other community detection metrics would improve its accuracy and reduce instability of LPA. Considering this point, in this paper we tried to use edge betweenness centrality to improve LPA performance. On the other hand, calculating edge betweenness centrality is expensive, so as an alternative metric, we try to use local edge betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge Betweenness). Experimental results on both real-world and benchmark networks show that LPA-LEB possesses higher accuracy and stability than LPA when detecting community structures in networks.Comment: 6 page