1. A. Longa, G. Cencetti, B. Lepri, and A. Passerini, ‘‘An efficient procedure 820 for mining egocentric temporal motifs,’’ Data Mining Knowl. Discovery, 821 vol. 36, no. 1, pp. 355–378, Jan, 2022.
2. A. Ficara, L. Cavallaro, F. Curreri, G. Fiumara, P. De Meo, O. Bagdasar, 834 W. Song, and A.Liotta, ‘‘Criminal networks analysis in missing data 835 scenarios through graph distances,’’ PLoSONE, vol. 16, no. 8, Aug. 2021, 836 Art. no. e0255067.
3. L. G. Singh, A. Mitra, and S. R. Singh, ‘‘Sentiment analysis of tweets 848 using heterogeneous multi-layer network representation and embedding,’’ 849 in Proc. Conf. Empirical Methods Natural Lang. Process. (EMNLP), 2020, 850 pp. 8932–8946.
4. O. Habimana, Y. Li, R. Li, X. Gu, and G. Yu, ‘‘Sentiment analysis using 864 deep learning approaches: An overview,’’ Sci. China Inf. Sci., vol. 63, no. 1, 865 pp. 1–36, 2020.
5. J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, ‘‘BERT: Pre-training 875 of deep bidirectional transformers for language understanding,’’ 2018, 876 arXiv:1810.04805.