# Finding Top-\(k\) Nodes for Temporal Closeness in Large Temporal Graphs
## Appendix A Further Experiments
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Name** & **APX-32** & **APX-64** & **APX-128** & **APX-256** & **APX-512** & **APX-1024** \\ \hline topology & 0.976 & 0.982 & 0.986 & 0.989 & 0.991 & 0.992 \\ adult & 0.920 & 0.936 & 0.958 & 0.968 & 0.974 & 0.979 \\ adventure & 0.920 & 0.938 & 0.948 & 0.955 & 0.960 & 0.963 \\ all & 0.954 & 0.964 & 0.972 & 0.977 & 0.981 & 0.983 \\ animation & 0.854 & 0.890 & 0.906 & 0.914 & 0.920 & 0.925 \\ biography & 0.624 & 0.747 & 0.829 & 0.858 & 0.869 & 0.876 \\ comedy & 0.941 & 0.954 & 0.961 & 0.968 & 0.973 & 0.975 \\ family & 0.717 & 0.811 & 0.864 & 0.879 & 0.890 & 0.898 \\ fantasy & 0.631 & 0.757 & 0.820 & 0.864 & 0.883 & 0.894 \\ history & 0.646 & 0.761 & 0.823 & 0.867 & 0.880 & 0.891 \\ music & 0.613 & 0.760 & 0.829 & 0.851 & 0.861 & 0.866 \\ musical & 0.813 & 0.884 & 0.915 & 0.926 & 0.938 & 0.944 \\ mystery & 0.808 & 0.860 & 0.901 & 0.917 & 0.925 & 0.929 \\ scifi & 0.628 & 0.721 & 0.803 & 0.842 & 0.855 & 0.865 \\ war & 0.782 & 0.855 & 0.894 & 0.913 & 0.925 & 0.933 \\ western & 0.927 & 0.947 & 0.957 & 0.965 & 0.972 & 0.976 \\ \hline election & 0.903 & 0.930 & 0.949 & 0.962 & 0.971 & 0.978 \\ facebook & 0.942 & 0.957 & 0.967 & 0.973 & 0.981 & 0.984 \\ linux & 0.959 & 0.970 & 0.978 & 0.982 & 0.985 & 0.988 \\ adelaide & 0.847 & 0.894 & 0.932 & 0.957 & 0.970 & 0.979 \\ belfast & 0.721 & 0.768 & 0.795 & 0.863 & 0.906 & 0.935 \\ berlin & 0.869 & 0.904 & 0.940 & 0.958 & 0.970 & 0.979 \\ bordeaux & 0.725 & 0.797 & 0.874 & 0.918 & 0.944 & 0.962 \\ brisbane & 0.872 & 0.908 & 0.952 & 0.971 & 0.980 & 0.985 \\ canberra & 0.811 & 0.860 & 0.911 & 0.935 & 0.951 & 0.966 \\ detroit & 0.804 & 0.860 & 0.905 & 0.941 & 0.962 & 0.973 \\ dublin & 0.805 & 0.865 & 0.902 & 0.935 & 0.957 & 0.970 \\ grenoble & 0.698 & 0.731 & 0.778 & 0.845 & 0.907 & 0.941 \\ helsinki & 0.843 & 0.873 & 0.901 & 0.926 & 0.951 & 0.967 \\ kuopio & 0.759 & 0.806 & 0.859 & 0.898 & 0.927 & 0.949 \\ lisbon & 0.845 & 0.872 & 0.909 & 0.938 & 0.954 & 0.968 \\ luxembourg & 0.885 & 0.915 & 0.939 & 0.955 & 0.969 & 0.978 \\ melbourne & 0.696 & 0.711 & 0.773 & 0.841 & 0.894 & 0.960 \\ nantes & 0.710 & 0.751 & 0.813 & 0.880 & 0.927 & 0.949 \\ palermo & 0.791 & 0.850 & 0.898 & 0.928 & 0.950 & 0.964 \\ paris & 0.696 & 0.736 & 0.794 & 0.891 & 0.944 & 0.966 \\ prague & 0.833 & 0.852 & 0.900 & 0.926 & 0.947 & 0.962 \\ rennes & 0.754 & 0.759 & 0.842 & 0.899 & 0.934 & 0.955 \\ rome & 0.825 & 0.862 & 0.917 & 0.949 & 0.966 & 0.976 \\ sydney & 0.831 & 0.870 & 0.898 & 0.941 & 0.965 & 0.977 \\ toulouse & 0.758 & 0.770 & 0.825 & 0.890 & 0.943 & 0.959 \\ turku & 0.842 & 0.881 & 0.923 & 0.946 & 0.961 & 0.973 \\ venice & 0.812 & 0.868 & 0.897 & 0.934 & 0.951 & 0.965 \\ Winnipeg & 0.867 & 0.915 & 0.947 & 0.963 & 0.973 & 0.981 \\ \hline twitter * & 0.637 & 0.857 & 0.922 & 0.959 & 0.973 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Average Kendall’s \(\tau\) values for the graphs in our dataset: the Kendall’s \(\tau\) is computed by referring to the ranking computed by exact, (the cell marked with * is an estimation) apart from the twitter graph, where we refer to the ranking computed by apx-1024.