… embeddings for applications such as node classification. To solve this problem, in this …
a novel network embedding framework, Discriminative Streaming Network Embedding (DimSim). …

… Considering that not all events in the newly arrived stream are unseen, we precalculate the
embeddings of already known clusters to detect them quickly. We call this inference process …

… by the generator though a neural network. We embed u with matrix E and embed v with matrix
F, … Irgan: A minimax game for unifying generative and discriminative information retrieval …

… , we first present an online network embedding algorithm to alleviate this problem by obtaining
the … structure, but also uncovers discriminative power inferred from labeled nodes that can …

… get task and thus often leads to suboptimal embeddings. In particular, for our focus … embeddings
to be both representing the topological structure of the network actors and discriminative …

… iteration has been reached, one can employ the optimized network embedding parameters
𝜃𝑒 ∗ to generate label-discriminative and networkinvariant node representations across …

… We further explore the role of adversarial learning for this task, and propose a second
unsupervised deep model which learns by discriminating the structure and the attribute based …

… The goal of this work is to train discriminative cross-modal embeddings without access to …
We build on earlier work to train embeddings that are more discriminative for uni-modal …

… proposing a novel dynamic attributed network embedding framework - DANE. … embedding
and then leverages matrix perturbation theory to maintain the freshness of the end embedding …

… across different networks. To this end, a novel cross-network deep network embedding (…
domain adaptation into deep network embedding in order to learn label-discriminative and …