Networks has the potential to increase the explanatory power of models. The content of this post, and accompanying paper, quantifies the size of this effect for predicting the financial health of companies. It is my hope that it can help drive more research into networks and enable more organizations to develop better models and understand interdependencies to a greater extent.
Companies do not operate in a vacuum. As companies move towards an increasingly specialized production function and their reach is becoming truly global, their aptitude in managing and shaping their inter-organizational network is a determining factor in measuring their health. Current models of company financial health often lack variables explaining the inter-organizational network, and as such, assume that (1) all networks are the same and (2) the performance of partners do not impact companies. This paper aims to be a first step in the direction of removing these assumptions. Specifically, the impact is illustrated by examining the effects of customer and supplier concentrations and partners’ credit risk on credit-default swap (CDS) spreads while controlling for credit risk and size. We rely upon supply-chain data from Bloomberg that provides insight into companies’ relationships. The empirical results show that a well diversified customer network lowers CDS spread, while having stable partners with low default probabilities increase spreads. The latter result suggests that successful companies do not focus on building a stable eco-system around themselves, but instead focus on their own profit maximization at the cost of the financial health of their suppliers’ and customers’. At a more general level, the results indicate the importance of considering the inter-organizational networks, and highlight the value of including network variables in credit risk models.
A surprising finding when analysing airport networks is the importance of Anchorage airport in Alaska. In fact, it is the most central airport in the network when applying betweenness! I do not believe this finding is completely accurate due to two reasons: (1) there is a potential for measurement error when not including tie weights (i.e., assigning the same importance to the connection between London Heathrow and New York’s JFK as to the connection between Pack Creek Airport and Sitka Harbor Sea Plane Base in Alaska), and (2) relying on US data only leads to sample selection as the airport network is a global system. This post highlights how to use a weighted betweenness measure as well as the extent of the sample selection issue.
A central metric in network research is the number of ties each node has, degree. Degree has been generalised to weighted networks as the sum of tie weights (Barrat et al., 2004), and as a function of the number of ties and the sum of their weights (Opsahl et al., 2010). However, all these measures are insensitive to variation in the tie weights. As such, the two nodes in this diagram would always have the same degree score. This post showcases a new measure that uses a tuning parameter to control whether variation should be taken favourable or discount the degree centrality score of a focal node.
A paper called “Node centrality in weighted networks: Generalizing degree and shortest paths” that I have co-authored will be published in Social Networks. Ties often have a strength naturally associated with them that differentiate them from each other. Tie strength has been operationalized as weights. A few network measures have been proposed for weighted networks, including three common measures of node centrality: degree, closeness, and betweenness. However, these generalizations have solely focused on tie weights, and not on the number of ties, which was the central component of the original measures. This paper proposes generalizations that combine both these aspects. We illustrate the benefits of this approach by applying one of them to Freeman’s EIES dataset.
A key node centrality measure in networks is closeness centrality (Freeman, 1978; Wasserman and Faust, 1994). It is defined as the inverse of farness, which in turn, is the sum of distances to all other nodes. As the distance between nodes in disconnected components of a network is infinite, this measure cannot be applied to networks with disconnected components (Opsahl et al., 2010; Wasserman and Faust, 1994). This post highlights a possible work-around, which allows the measure to be applied to these networks and at the same time maintain the original idea behind the measure.
Similar to the motivation of the global clustering coefficient that I proposed in Clustering in two-mode networks, the local clustering coefficient is biased if applied to a projection of a two-mode network. It is biased in the sense that the randomly expected value is not obtained on the projection of a random two-mode network. To overcome this methodological bias, I redefine the local clustering coefficient for two-mode networks. The new coefficient is a mix between the global clustering coefficient for two-mode networks and Barrat’s (2004) local coefficient for a weighted one-mode network. The coefficient is tested on Davis’ (1940) Southern Women dataset.