With the modern advancements in Deep Learning architectures, and abundant research consistently being put forward in areas such as computer vision, natural language processing and forecasting. Models are becoming complicated and datasets are growing exponentially in size demanding high performing and faster computing machines from researchers and engineers. TensorFlow provides a wide range of distributed deep learning high-level APIs to address this issue, that can scale deep learning training from one machine to more than one. In this paper, we have investigated the performance of computing clusters utilizing those APIs. We created clusters of different sizes and discuss performance issues of distributed deep learning under high latency and poor communication conditions. To address the challenge of finding the optimal cluster for fast distributed deep learning, we have proposed a recommendation system, that can provide an optimal cluster size for fastest training time, given batch size and networking latency. Our results show that using a 2 machine cluster is both faster and cheaper than a four machine cluster for certain algorithms when network delay is high.

The uncertainty underlying real-world phenomena has attracted attention toward statistical analysis approaches. In this regard, many problems can be modeled as networks. Thus, the statistical analysis of networked problems has received special attention from many researchers in recent years. Exponential Random Graph Models, known as ERGMs, are one of the popular statistical methods for analyzing the graphs of networked data. ERGM is a generative statistical network model whose ultimate goal is to present a subset of networks with particular characteristics as a statistical distribution. In the context of ERGMs, these graph’s characteristics are called statistics or configurations. Most of the time they are the number of repeated subgraphs across the graphs. Some examples include the number of triangles or the number of cycle of an arbitrary length. Also, any other census of the graph, as with the edge density, can be considered as one of the graph’s statistics. In this review paper, after explaining the building blocks and classic methods of ERGMs, we have reviewed their newly presented approaches and research papers. Further, we have conducted a comprehensive study on the applications of ERGMs in many research areas which to the best of our knowledge has not been done before. This review paper can be used as an introduction for scientists from various disciplines whose aim is to use ERGMs in some networked data in their field of expertise.

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