Adjectives For Graphs
Discover the most popular adjectives for describing graphs, complete with example sentences to guide your usage.
Updated on March 16, 2024
Graphs are a fundamental tool in data visualization, conveying complex information in an accessible format. The adjectives used with 'graphs'—such as 'conceptual', 'random', 'directed', 'simple', and 'undirected'—highlight the diverse approaches to data representation. A 'conceptual' graph manifests abstract ideas, whereas a 'random' graph showcases unpredictability within data sets. 'Directed' and 'undirected' graphs play pivotal roles in network theory, indicating the presence or absence of direction in connections. A 'simple' graph, on the other hand, emphasizes clarity and minimalism. These adjectives enrich our understanding of graphs, suggesting their adaptability and precision in illustrating relationships. Explore the full extent of descriptive possibilities with our comprehensive list of adjectives used with 'graphs'.
| such | The program prints such graphs in the console. |
| conceptual | Conceptual graphs are a formal knowledge representation language that uses graphs to represent concepts and their relationships. |
| random | Random graphs have vertices and edges that are connected randomly. |
| directed | The directed graphs are acyclic, meaning they contain no cycles. |
| simple | Simple graphs are mathematical structures that represent pairwise relations between elements of a set. |
| undirected | The undirected graphs show the relationships between objects without specifying the direction of the relationship. |
| following | Examine the following graphs to conclude the pattern. |
| acyclic | Acyclic graphs are useful for many different purposes. |
| bipartite | The community structure of bipartite graphs is used to uncover the hierarchical organization of a network. |
| linear | Those linear graphs are difficult to understand. |
| dimensional | The research team developed several curves on dimensional graphs to illustrate the relation of the load with vertical deflection. |
| corresponding | The corresponding graphs are shown below. |
| complete | Complete graphs are graphs in which every pair of vertices is connected by an edge. |
| similar | The two functions have similar graphs |
| line | The line graphs in the presentation clearly depicted the changes in sales. |
| regular | "Regular graphs" are those graphs in which every vertex has the same degree. |
| connected | The connected graphs have many applications in computer science. |
| separate | The data was plotted on separate graphs |
| interval | Interval graphs represent relationships between objects that overlap in time or space. |
| individual | Each project group is required to submit 2 individual graphs that provide an analysis of their findings. |
| time | The time graphs show the relationship between distance and time. |
| finite | Finite graphs are graphs that have a finite number of vertices and edges. |
| lower | The lower graphs show the distribution in the three age groups. |
| weighted | The weighted graphs are used to calculate the shortest path between two vertices. |
| statistical | The statistical graphs were used to illustrate the data analysis. |
| typical | The researcher analyzed the typical graphs about the provided data. |
| arbitrary | The arbitrary graphs are used to study the structure of complex networks. |
| sparse | Sparse graphs are those in which the number of edges is significantly less than the number of possible edges. |
| cumulative | Cumulative graphs such as trend graphs, show data accumulation over time. |
| planar | Complex networks are often represented as graphs, where nodes represent elements of the system and the edges between them represent interactions, and planar graphs are graphs that can be drawn on a plane without any edges crossing. |
| signal | The researchers used signal graphs to visualize the network's behavior. |
| sub | The sub graphs are used to represent the relation between two or more variables. |
| existential | The existential graphs are a graphical representation of first-order logic. |
| isomorphic | The two graphs are isomorphic graphs |
| complex | The problem of finding optimal homomorphisms between complex graphs is NP-complete |
| chordal | Chordal graphs are undirected graphs that can be decomposed into cliques. |
| multiple | The study used multiple graphs to illustrate the findings. |
| upper | Researchers often make heavy use of upper graphs and lower graphs. |
| possible | The analysis of possible graphs can be useful in determining the best way to represent a given data set. |
| numerous | The researcher used numerous graphs to illustrate the data. |
| appropriate | I generated appropriate graphs to illustrate the data. |
| logarithmic | Logarithmic graphs are used to represent data that has an exponential relationship. |
| top | |
| geometric | |
| cyclic | Cyclic graphs are graphs with cycles, meaning paths that begin and end at the same vertex. |
| dense | Dense graphs are full of edges, with many connections between nodes. |
| bottom | The bottom graphs illustrate the data distribution across different values. |
| molecular | The molecular graphs were used to predict the properties of the compounds. |
| parallel | The two parallel graphs exhibit similar trends. |
| hierarchical | The hierarchical graphs are a powerful tool for representing complex data structures. |
| abstract | The complexity of abstract graphs cannot be underestimated, as they provide a way to model complex relationships in a wide variety of domains. |
| above | Compare different themes and their popularity over time based on the above graphs |
| dual | One of the randomly selected graphs is labelled with virtual atoms, which can then be matched to the dual graphs of their neighbours. |
| pictorial | These pictorial graphs illustrate the rapid increase in population. |
| standard | Standard graphs are used to represent data in a visual way. |
| chinese | The Chinese graphs are beautiful. |
| perfect | Perfect graphs are a class of graphs that have a number of interesting properties. |
| relational | Relational graphs depict links between entities. |
| circular | These circular graphs show the data distribution over time and across categories. |
| dynamic | These dynamic graphs make it easy to visualize data and identify trends. |
| causal | Causal graphs are a useful tool for representing the relationships between variables in a system. |
| infinite | The theory of infinite graphs is a generalization of the classical theory of finite graphs. |
| cubic | "In graph theory, cubic graphs are graphs in which every vertex has degree three." |
| canonical | Canonical graphs are a type of graphs that represent a canonical form of a given graphs. |
| straight | The straight graphs show the relationship between two variables. |
| partial | The partial graphs illustrate the differences in average relative expression between the alternative splicing isoforms or non-coding variants and their cognate protein-coding genes. |
| colored | The brightly colored graphs were easy to read and understand. |
| useful | These useful graphs will help visualize the data. |
| comparative | The data is visualized using comparative graphs that show changes over time. |
| respective | |
| complicated | The complicated graphs were difficult to decipher. |
| fuzzy | The researchers applied fuzzy graphs to model the uncertainties in the network. |
| generated | The researchers generated graphs of the data to glean insights. |
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