No Arabic abstract
Understanding prospective clients becomes increasingly important as companies aim to enlarge their market bases. Traditional approaches typically treat each client in isolation, either studying its interactions or similarities with existing clients. We propose the Client Network, which considers the entire client ecosystem to predict the success of sale pitches for targeted clients by complex network analysis. It combines a novel ranking algorithm with data visualization and navigation. Based on historical interaction data between companies and clients, the Client Network leverages organizational connectivity to locate the optimal paths to prospective clients. The user interface supports exploring the client ecosystem and performing sales-essential tasks. Our experiments and user interviews demonstrate the effectiveness of the Client Network and its success in supporting sellers day-to-day tasks.
Federated Learning (FL), arising as a novel secure learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a fraction of available clients are chosen to participate and the selection decision might have a significant effect on the training efficiency, as well as the final model performance. In this paper, we investigate the client selection problem under a volatile context, in which the local training of heterogeneous clients is likely to fail due to various kinds of reasons and in different levels of frequency. Intuitively, too much training failure might potentially reduce the training efficiency, while too much selection on clients with greater stability might introduce bias, and thereby result in degradation of the training effectiveness. To tackle this tradeoff, we in this paper formulate the client selection problem under joint consideration of effective participation and fairness. Further, we propose E3CS, a stochastic client selection scheme on the basis of an adversarial bandit solution, and we further corroborate its effectiveness by conducting real data-based experiments. According to the experimental results, our proposed selection scheme is able to achieve up to 2x faster convergence to a fixed model accuracy while maintaining the same level of final model accuracy, in comparison to the vanilla selection scheme in FL.
Graph mining to extract interesting components has been studied in various guises, e.g., communities, dense subgraphs, cliques. However, most existing works are based on notions of frequency and connectivity and do not capture subjective interestingness from a users viewpoint. Furthermore, existing approaches to mine graphs are not interactive and cannot incorporate user feedbacks in any natural manner. In this paper, we address these gaps by proposing a graph maximum entropy model to discover surprising connected subgraph patterns from entity graphs. This model is embedded in an interactive visualization framework to enable human-in-the-loop, model-guided data exploration. Using case studies on real datasets, we demonstrate how interactions between users and the maximum entropy model lead to faster and explainable conclusions.
Online user innovation communities are becoming a promising source of user innovation knowledge and creative users. With the purpose of identifying valuable innovation knowledge and users, this study constructs an integrated super-network model, i.e., User Innovation Knowledge Super-Network (UIKSN), to integrate fragmented knowledge, knowledge fields, users and posts in an online community knowledge system. Based on the UIKSN, the core innovation knowledge, core innovation knowledge fields, core creative users, and the knowledge structure of individual users were identified specifically. The findings help capture the innovation trends of products, popular innovations and creative users, and makes contributions on mining, and integrating and analyzing innovation knowledge in community based innovation theory.
Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to dirty-label data poisoning adversarial attacks. We claim that the federated learning model has to avoid those kind of adversarial attacks through filtering out the clients that manipulate the local data. We propose a dynamic federated learning model that dynamically discards those adversarial clients, which allows to prevent the corruption of the global learning model. We evaluate the dynamic discarding of adversarial clients deploying a deep learning classification model in a federated learning setting, and using the EMNIST Digits and Fashion MNIST image classification datasets. Likewise, we analyse the capacity of detecting clients with poor data distribution and reducing the number of rounds of learning by selecting the clients to aggregate. The results show that the dynamic selection of the clients to aggregate enhances the performance of the global learning model, discards the adversarial and poor clients and reduces the rounds of learning.
Graphs representing real world systems may be studied from their underlying community structure. A community in a network is an intuitive idea for which there is no consensus on its objective mathematical definition. The most used metric in order to detect communities is the modularity, though many disadvantages of this parameter have already been noticed in the literature. In this work, we present a new approach based on a different metric: the surprise. Moreover, the biases of different community detection algorithms and benchmark networks are thoroughly studied, identified and commented about.