What Does Network Topology Mean?
Network topology refers to the physical and logical arrangement of nodes and connections in a computing network.
Physical topology describes the layout of devices and cables, and logical topology describes the way in which data is transmitted within the network — regardless of the physical layout.
Physical and logical topologies play an important role in the overall performance, scalability and security of a network. Each topology has its own advantages and disadvantages, and the choice of topology depends on the specific requirements of the network.
Techopedia Explains Network Topology
Physical network topologies can be categorized into five basic models:
- Bus Topology: All the devices/nodes are connected to a single communication line called a bus. The bus serves as a backbone that connects all the devices in the network. This is a simple, low-cost topology, but if the bus breaks down, the single point of failure (SPOF) can disable the entire network.
- Star Topology: All the nodes in the network are connected to a central hub or switch. The hub or switch acts as a central point for communication, and each device has a separate connection to it. This topology is popular because it is easy to manage, and a break in one connection does not affect the other devices.
- Ring Topology: All the network’s nodes are connected in a closed loop and data flows in one direction around the loop. Ring topologies are usually designed with redundancy to ensure that a break in the ring does not bring the entire network down.
- Tree Topology: All the nodes are arranged in a hierarchical structure that resembles a physical tree. The central node at the top of the tree (usually a hub or switch) can connect to multiple other nodes, which in turn can connect to other additional nodes.
- Mesh Topology: Each node is directly connected to some (or all) the other network nodes in a WiFi mesh system. The redundancy makes this type of topology highly fault-tolerant, but it requires more bandwidth and can be expensive to implement.
Network Topologies Used in AI and Machine Learning
Network topologies used in artificial intelligence (AI) and machine learning (ML) include:
Feedforward neural networks: These networks consist of an input layer, one or more hidden layers and an output layer. Data is passed through the network in one direction, from input to output, and there are no feedback loops.
Convolutional neural networks (CNNs): These networks are commonly used for image recognition and classification tasks. They use convolutional layers to extract features from input data and pooling layers to reduce the size of the input data.
Recurrent neural networks (RNNs): These networks are commonly used for sequential data, such as text or speech. They have feedback loops that allow the network to use previous predictions as input for subsequent predictions.
Long short-term memory (LSTM) networks: These are a type of RNN that can maintain a long-term memory of previous inputs.
Autoencoders: These networks are used for unsupervised learning and data compression. They consist of an encoder network that compresses input data into a smaller representation, and a decoder network that reconstructs the original data from the compressed representation.
Generative adversarial networks (GANs): These networks are used for generating new data, such as images or text. They consist of two networks: a generator network that generates new data and a discriminator network that attempts to distinguish the generated data from real data.
Transformers: These networks use self-attention mechanisms to selectively focus on different parts of the input data when making predictions. They are commonly used for natural language processing tasks such as language translation and text classification.
Network Topology and Opacity
In the most modern systems, networks have become so complex that traditional topologies now apply in different ways. One of these phenomena is the use of opaque systems to foil hackers or outside cyberattacks. Some experts are now suggesting that by shielding the IP addresses and isolating different parts of the network into segments, companies can practice better cybersecurity hygiene. All of that continues to change how network topologies are used.