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Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution shift. There is still significant ambiguity about why…
Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of non-linear overlap cost that penalizes congestion. Routing becomes increasingly more difficult as the number of selected…
The size of computer networks, along with their bandwidths, is growing exponentially. To support these large, high-speed networks, it is neccessary to be able to forward packets in a few microseconds. One part of the forwarding operation…
Federated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the major challenges. Most relative arts focus on traditional…
For the List Accessing Problem, Move-To-Front(MTF) algorithm has been proved to be the best performing online list accessing algorithm till date in the literature[10]. In this paper, we have made a comprehensive analysis of MTF algorithm…
The presentation of results from Systematic Literature Reviews (SLRs) is generally done using tables. Prior research suggests that results summarized in tables are often difficult for readers to understand. One alternative to improve…
Random access networks have long been observed to suffer from low throughput if nodes' access strategy is not properly designed. To improve the throughput performance, learning-based approaches, with which each node learns from the…
We consider online algorithms for pull-based broadcast scheduling. In this setting there are n pages of information at a server and requests for pages arrive online. When the server serves (broadcasts) a page p, all outstanding requests for…
The web graph is a commonly-used network representation of the hyperlink structure of a website. A network of similar structure to the web graph, which we call the session graph has properties that reflect the browsing habits of the agents…
Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery. Machine learning methods utilized in these applications would be better…
Analytical queries over RDF data are becoming prominent as a result of the proliferation of knowledge graphs. Yet, RDF databases are not optimized to perform such queries efficiently, leading to long processing times. A well known technique…
In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness works on graphs mainly focus on protecting sensitive…
We present a new fast all-pairs shortest path algorithm for unweighted graphs. In breadth-first search which is said to representative and fast in unweighted graphs, the average number of accesses to adjacent vertices (expressed by…
Graph databases in many applications---semantic web, transport or biological networks among others---are not only large, but also frequently modified. Evaluating graph queries in this dynamic context is a challenging task, as those queries…
In this work we unify two existing lines of work towards cache analysis for non-LRU policies. To this end, we extend the notion of competitiveness to block competitiveness and systematically analyze the competitiveness and block…
The emerging theory of graph limits exhibits an analytic perspective on graphs, showing that many important concepts and tools in graph theory and its applications can be described more naturally (and sometimes proved more easily) in…
An ordering of the vertices of a graph is \emph{connected} if every vertex (but the first) has a neighbor among its predecessors. The greedy colouring algorithm of a graph with a connected order consists in taking the vertices in order, and…
We introduce and simulate the random walk that adapts move strategies according to local node preferences on a directed graph. We consider graphs with double-hierarchical connectivity and variable wiring diagram in the universality class of…
We study spreading processes in temporal graphs, i. e., graphs whose connections change over time. These processes naturally model real-world phenomena such as infectious diseases or information flows. More precisely, we investigate how…
Preference restrictions have played a significant role in computational social choice. This paper studies a framework that connects preference restrictions with classical graph search paradigms. We model candidates as vertices of a graph…