Neural Interactome: Interactive Simulation of a Neuronal System

Connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, developed a real-time framework, called Neural Interactome1,2, to simultaneously visualize and interact with the structure and dynamics of such networks. Neural Interactome is a cross-platform framework, which combines graph visualization with the simulation of neural dynamics, or experimentally recorded multi neural time series, to allow application of stimuli to neurons to examine network responses. In addition, Neural Interactome supports structural changes, such as disconnection of neurons from the network (ablation feature). Neural dynamics can be explored on a single neuron level (using a zoom feature), back in time (using a review feature), and recorded (using presets feature). The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics. We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism with resolved connectome and neural dynamics. We show that Neural Interactome assists in studying neural response patterns associated with locomotion and other stimuli. In particular, we demonstrate how stimulation and ablation help in identifying neurons that shape particular dynamics. We examine scenarios that were experimentally studied, such as touch response circuit, and explore new scenarios that did not undergo elaborate experimental studies

Mining and Managing Large-Scale Temporal Graphs

Large-scale temporal graphs are everywhere in our daily life. From online social networks, mobile networks, brain networks to computer systems, entities in these large complex systems communicate with each other, and their interactions evolve over time. Unlike traditional graphs, temporal graphs are dynamic: both topologies and attributes on nodes/edges may change over time. On the one hand, the dynamics have inspired new applications that rely on mining and managing temporal graphs. On the other hand, the dynamics also raise new technical challenges. First, it is difficult to discover or retrieve knowledge from complex temporal graph data. Second, because of the extra time dimension, we also face new scalability problems. To address these new challenges, we need to develop new methods that model temporal information in graphs so that we can deliver useful knowledge, new queries with temporal and structural constraints where users can obtain the desired knowledge, and new algorithms that are cost-effective for both mining and management tasks.In this dissertation, we discuss our recent works on mining and managing large-scale temporal graphs.First, we investigate two mining problems, including node ranking and link prediction problems. In these works, temporal graphs are applied to model the data generated from computer systems and online social networks. We formulate data mining tasks that extract knowledge from temporal graphs. The discovered knowledge can help domain experts identify critical alerts in system monitoring applications and recover the complete traces for information propagation in online social networks. To address computation efficiency problems, we leverage the unique properties in temporal graphs to simplify mining processes. The resulting mining algorithms scale well with large-scale temporal graphs with millions of nodes and billions of edges. By experimental studies over real-life and synthetic data, we confirm the effectiveness and efficiency of our algorithms.Second, we focus on temporal graph management problems. In these study, temporal graphs are used to model datacenter networks, mobile networks, and subscription relationships between stream queries and data sources. We formulate graph queries to retrieve knowledge that supports applications in cloud service placement, information routing in mobile networks, and query assignment in stream processing system. We investigate three types of queries, including subgraph matching, temporal reachability, and graph partitioning. By utilizing the relatively stable components in these temporal graphs, we develop flexible data management techniques to enable fast query processing and handle graph dynamics. We evaluate the soundness of the proposed techniques by both real and synthetic data. Through these study, we have learned valuable lessons. For temporal graph mining, temporal dimension may not necessarily increase computation complexity; instead, it may reduce computation complexity if temporal information can be wisely utilized. For temporal graph management, temporal graphs may include relatively stable components in real applications, which can help us develop flexible data management techniques that enable fast query processing and handle dynamic changes in temporal graphs