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Identifying influential nodes in a network is arguably one of the most important tasks in graph mining and network analysis. A large variety of centrality measures, all aiming at correctly quantifying a node’s importance in the network, have been formulated in the literature. One of the most cited ones is the betweenness centrality, formally introduced by Freeman (Sociometry, 1977). On the other hand, researchers have recently been very interested in capturing the dynamic nature of real-world networks by studying temporal graphs, rather than static ones. Clearly, centrality measures, including the betweenness centrality, have also been extended to temporal graphs. Buß et al. (KDD, 2020) gave algorithms to compute various notions of temporal betweenness centrality, including the perhaps most natural one - shortest temporal betweenness. Their algorithm computes centrality values of all nodes in time O(n³ T²), where n is the size of the network and T is the total number of time steps. For real-world networks, which easily contain tens of thousands of nodes, this complexity becomes prohibitive. Thus, it is reasonable to consider proxies for shortest temporal betweenness rankings that are more efficiently computed, and, therefore, allow for measuring the relative importance of nodes in very large temporal graphs. In this paper, we compare several such proxies on a diverse set of real-world networks. These proxies can be divided into global and local proxies. The considered global proxies include the exact algorithm for static betweenness (computed on the underlying graph), prefix foremost temporal betweenness of Buß et al., which is more efficiently computable than shortest temporal betweenness, and the recently introduced approximation approach of Santoro and Sarpe (WWW, 2022). As all of these global proxies are still expensive to compute on very large networks, we also turn to more efficiently computable local proxies. Here, we consider temporal versions of the ego-betweenness in the sense of Everett and Borgatti (Social Networks, 2005), standard degree notions, and a novel temporal degree notion termed the pass-through degree, that we introduce in this paper and which we consider to be one of our main contributions. We show that the pass-through degree, which measures the number of pairs of neighbors of a node that are temporally connected through it, can be computed in nearly linear time for all nodes in the network and we experimentally observe that it is surprisingly competitive as a proxy for shortest temporal betweenness.