
Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks
We recently introduced a formalism for the modeling of temporal networks...
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Estimating Node Similarity by Sampling Streaming Bipartite Graphs
Bipartite graph data increasingly occurs as a stream of edges that repre...
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Counting Butterfies from a Large Bipartite Graph Stream
We consider the estimation of properties on massive bipartite graph stre...
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Searching for squarecomplementary graphs: nonexistence results and complexity of recognition
A graph is squarecomplementary (squco, for short) if its square and com...
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Sparse graphs are nearbipartite
A multigraph G is nearbipartite if V(G) can be partitioned as I,F such ...
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Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
The von Neumann graph entropy (VNGE) facilitates the measure of informat...
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Greedy Bipartite Matching in Random Type Poisson Arrival Model
We introduce a new random input model for bipartite matching which we ca...
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Incremental Sparse TFIDF & Incremental Similarity with Bipartite Graphs
In this report, we experimented with several concepts regarding text streams analysis. We tested an implementation of Incremental Sparse TFIDF (ISTFIDF) and Incremental Cosine Similarity (ICS) with the use of bipartite graphs. We are using bipartite graphs  one type of node are documents, and the other type of nodes are words  to know what documents are affected with a word arrival at the stream (the neighbors of the word in the graph). Thus, with this information, we leverage optimized algorithms used for graphbased applications. The concept is similar to, for example, the use of hash tables or other computer science concepts used for fast access to information in memory.
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