We introduce applications of established methods in time-series and network analysis that we jointly apply here for the kinematic study "of gesture ensembles". We define a gesture ensemble as the set of gestures produced during discourse by a single person or a group of persons. Here we are interested in how gestures kinematically relate to one another. We use a bivariate time-series analysis called dynamic time warping to assess how similar each gesture is to other gestures in the ensemble in terms of their velocity profiles (as well as studying multivariate cases with gesture velocity and speech amplitude envelope profiles). By relating each gesture event to all other gesture events produced in the ensemble, we obtain a weighted matrix that essentially represents a network of similarity relationships. We can therefore apply network analysis that can gauge, for example, how diverse or coherent certain gestures are with respect to the gesture ensemble. We believe these analyses promise to be of great value for gesture studies, as we can come to understand how low-level gesture features (kinematics of gesture) relate to the higher-order organizational structures present at the level of discourse.