Alphacore

Abstract

Core decomposition in networks has proven useful for evaluating the importance of nodes and communities in a variety of application domains, ranging from biology to social networks and finance. However, existing core decomposition algorithms have limitations in simultaneously handling multiple node and edge attributes. We propose a novel unsupervised core decomposition method that can be easily applied to directed and weighted networks. Our algorithm, AlphaCore, allows us to systematically and mathematically rigorously combine multiple node properties by using the notion of data depth. In addition, it can be used as a mixture of centrality measure and core decomposition. Compared to existing approaches, AlphaCore avoids the need to specify numerous thresholds or coefficients and yields meaningful quantitative and qualitative insights into the network structural organization. We evaluate AlphaCore's performance with a focus on financial, blockchain-based token networks, the social network Reddit and a transportation network of international flight routes. We compare our results with existing core decomposition and centrality algorithms. Using ground truth about node importance, we show that AlphaCore yields the best precision and recall results among core decomposition methods using the same input features. An implementation is available at https://github.com/friedhelmvictor/alphacore.