Figure - available from: The Journal of Supercomputing
This content is subject to copyright. Terms and conditions apply.
Source publication
In this era of big data, as the data size is scaling up, the need for computing power is exponentially increasing. However, most of the community detection algorithms in the literature are classified as global algorithms, which require access to the entire information of the network. These algorithms designed to work on a single machine cannot be d...
Similar publications
In this paper, we consider a distributed constrained optimization problem with delayed subgradient information over the time-varying communication network, where each agent can only communicate with its neighbors and the communication channel has a limited data rate. We propose an adaptive quantization method to address this problem. A mirror desce...
In this paper we study the distributed average consensus problem in multi-agent systems with directed communication links that are subject to quantized information flow. Specifically, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (i.e., the information stored, processed and exchanged betwe...
This paper addresses the problem of resilient average consensus in the presence of Byzantine agents in multi-agent networks. An event-triggered secure acceptance and broadcasting algorithm is proposed in which full knowledge of the network and high computational capabilities of each regular node are not required. The computational expense and commu...
We present a distributed solver for a large and important class of Laplacian systems that we call "one-sink" Laplacian systems. Specifically, our solver can produce solutions for systems of the form $L\mathbf{x} = \mathbf{b}$ where exactly one of the coordinates of $\mathbf{b}$ is negative. Our solver is an organically distributed algorithm that ta...
In this work, we investigate the application of Taylor expansions in reinforcement learning. In particular, we propose Taylor expansion policy optimization, a policy optimization formalism that generalizes prior work (e.g., TRPO) as a first-order special case. We also show that Taylor expansions intimately relate to off-policy evaluation. Finally,...
Citations
... Popular Community Detection Algorithms with low runtime complexities, such as Louvain, Label Propagation, and Infomap methods, have been implemented and compared on Peer-topeer (P2P) networks. Existing methods [6][7][8][9][10][11][12] mainly utilize only topological data and neglect the rich data obtained from the content data. As the size and complexity of P2P networks increases, more sophisticated techniques are needed to detect communities. ...
... In [9], the authors have proposed a method to monitor connections of known nodes in the network and then progressively discover other nodes through the analysis of their mutual contacts; instead of relying on the study of content characteristics or packet properties. In [10], the authors have proposed a Decentralized Iterative Community Clustering Approach (DICCA) to reveal the community structure for large networks using the LFR benchmark model. The proposed method identifies the community clusters from an entire network without the global knowledge of the network topology due to the use of the Parallel Decentralized Iterative Community Clustering Approach (PDICCA), a pipelined parallel implementation that transforms the serial process of the DICCA into a parallelized approach. ...
Community detection is essential in P2P network analysis as it helps identify connectivity structure, undesired centralization, and influential nodes. Existing methods primarily utilize topological data and neglect the rich content data. This paper proposes a technique combining topological and content data to detect communities inside the Bitcoin network using a deep feature representation algorithm and Deep Feedforward Autoencoders. Our results show that the Bitcoin network has a higher clustering coefficient, assortativity coefficient, and community structure than expected from a random P2P network. In the Bitcoin network, nodes prefer to connect to other nodes that share the same characteristics.
... Therefore, the proposed approach will consider attribute information and structure information. The structure information consists of shared neighbours information and connectivity information aspects of the network [3]. ...
... In case there are no good divisions existing, the least bad one will be identified as the solution. On the other hand, in the latter, the algorithm only divides the network when good divisions exist and leave the network undivided in case there are no good divisions existing [3,15]. ...
With the recent prevalence of information networks, the topic of community detection has gained much interest among researchers. In real-world networks, node attribute (content information) is also available in addition to topology information. However, the collected topology information for networks is usually noisy when there are missing edges. Furthermore, the existing community detection methods generally focus on topology information and largely ignore the content information. This makes the task of community detection for incomplete networks very challenging. A new method is proposed that seeks to address this issue and help improve the performance of the existing community detection algorithms by considering both sources of information, i.e. topology and content. Empirical results demonstrate that our proposed method is robust and can detect more meaningful community structures within networks having incomplete information, than the conventional methods that consider only topology information.
